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Archive for the ‘Artificial Intelligence – Breakthroughs in Theories and Technologies’ Category

Drug Development Process been Revolutionized by Artificial Intelligence (AI) Technologies

Curators: Stephen J. Williams, PhD and Aviva Lev-Ari, PhD, RN

 

The Voice of Stephen J. Williams, PhD

LPBI Group, CSO

PENDING

The Voice of Aviva Lev-Ari, PhD, RN

1.0 LPBI Group and 2.0 LPBI Group, Founder

With the advent of AI in the last 5-7 years in our fields: Pharmaceutical, Life Sciences and Medicine, LPBI Group had launched several initiatives to advance the frontier of knowledge by using our own contents repositories of +8 giga bytes for experimenting with Machine Learning (ML) technologies for Medical Text Analysis.

These AI Technologies include

  • Natural Language Processing (NLP): Statistical ML and Deep Learning ML
  • ChatGPT and GPT-4
  • Generative AI
A quote by Brad Power, Co-founder and CEO, Cancer Patient Lab made in January 2024
LPBI Group is in the admirable position of sitting on a treasure trove of medical literature that would be useful input in the current environment of customized ChatGPTs looking for reliable medical content.

In the Drug Development (DD) field, AI technologies are been employed  chiefly, for these tasks:

(a) Generation of molecular information libraries

(b) Explorations and combinatorial experiments on protein structures, and

(c) measurements of biochemical interactions

The A.I. learns from patterns in the data to suggest possible useful drug candidates, as if matching chemical keys to the right protein locks.

Because A.I. for drug development is powered by precise scientific data, toxic “hallucinations” are far less likely than with more broadly trained chatbots. And any potential drug must undergo extensive testing in labs and in clinical trials before it is approved for patients.

“Generative A.I. is transforming the field, but the drug-development process is messy and very human,” said David Baker, a biochemist and director of the Institute for Protein Design at the University of Washington.

As of December 2023,

  • 24 AI-discovered molecules had completed Phase I trials, with 21 of them being successful. This success rate of 80–90% is higher than the historical industry average of 40–65%. 
  • In Phase II trials, the success rate is around 40%, which is similar to the historical average. 

SOURCE

AI Overview

https://www.google.com/search?q=A.I.-developed+drugs+are+in+clinical+trials&oq=A.I.-developed+drugs+are+in+clinical+trials&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIHCAEQIRigATIHCAIQIRigATIHCAMQIRigAdIBCTIxNzZqMGoxNagCCLACAQ&sourceid=chrome&ie=UTF-8

 

Examples of drugs developed by AI technologies:

NCI definition of AI Drugs:

AI drug

A drug that blocks the activity of an enzyme called aromatase, which the body uses to make estrogen in the ovaries and other tissues. Blocking aromatase lowers the amount of estrogen made by the body, which may stop the growth of cancer cells that need estrogen to grow. AI drugs are used to treat some types of breast cancer or to keep it from coming back. They may also be used to help prevent breast cancer in some women who are at a high risk of developing it. Examples of AI drugs are anastrozole, letrozole, and exemestane. AI drugs are a type of hormone therapy. Also called aromatase inhibitor.
 

SOURCE

https://www.cancer.gov/publications/dictionaries/cancer-terms/def/ai-drug

 

More examples of AI Drugs, Drugs developed with AI technologies

#1: 

INS018_055
Developed by Insilico Medicine, a Hong Kong-based biotech startup, to treat idiopathic pulmonary fibrosis (IPF). IPF is a chronic lung disease that causes scarring and can be fatal if left untreated. In January 2023, Insilico Medicine announced positive results from a Phase I safety trial of INS018_055. In February 2023, the FDA granted breakthrough status to a small molecule inhibitor identified by Insilico Medicine’s AI platforms for the drug. As of November 2023, INS018_055 was in mid-stage trials in the US and China, with some results expected in early 2025.
 
The first fully A.I. -generated drug enters clinical trials in human patients. Insilico Medicine, a Hong Kong-based biotech startup with more than $400 million in funding, created the drug as a treatment for idiopathic pulmonary fibrosis, a chronic lung disease.Jun 29, 2023
 
  • Schrödinger’s AI-driven platform
    Uses predictive modeling to optimize the molecular structure of drugs. AI is expected to integrate more advanced simulation techniques, such as quantum computing, to more accurately predict molecular behavior.

Drug discovery software developer Schrodinger Inc. (NASDAQ: SDGR) stock has been trying to recover after plummeting over 80% off its all-time high of $117 in January 2021. Schrodinger’s artificial intelligence (AI) powered software technology platform utilizes physics-based modeling and sophisticated machine learning algorithms to help clients identify the suitable molecules to treat the desired ailments. Its programs can help predict the behavior of molecules and potential outcomes.

This entails finding suitable molecules that effectively target specific cells and proteins, transcend through cell walls, are absorbed and dissolved well without interfering with other drugs or producing bad reactions to other drugs, and are scalable.

Big Name Pharma Customers of Schrödinger, Inc.

Its technology platform allows for the faster and cheaper discovery of novel molecules with a higher success rate than traditional methods. Its clients include the top 20 pharmaceutical companies in the world, including Pfizer Inc. (NYSE: PFE)Merck & Co. Inc. (NYSE: MRK), Takeda, AstraZeneca PLC (NYSE: AZN), and GlaxoSmithKline plc  (NYSE: GSK). It closed new agreements with Eli Lilly & Co. (NYSE: LLY) and Otsuka Pharmaceuticals out of Tokyo, Japan.

SOURCE

Schrodinger is an AI-Powered Drug Discovery Developer to Watch

https://www.nasdaq.com/articles/schrodinger-is-an-ai-powered-drug-discovery-developer-to-watch

 

Schrodinger’s Pipelines include:

  • SGR-1505 (MALT1)

Hematologic Malignancies

DISCOVERYPRECLINICALPHASE 1
 
  • SGR-2921 (CDC7)

AML/MDS

DISCOVERYPRECLINICALPHASE 1
 
  • SGR-3515 (Wee1/Myt1)

Solid Tumors

DISCOVERYPRECLINICALPHASE 1
 
  • SOS1

Oncology

DISCOVERYPRECLINICALPHASE 1
 
  • PRMT5-MTA

Oncology

DISCOVERYPRECLINICALPHASE 1
 
  • EGFRC797S

Oncology

DISCOVERYPRECLINICALPHASE 1
 
  • NLRP3

Immunology

DISCOVERYPRECLINICALPHASE 1
 
  • LRRK2

Neurology

DISCOVERYPRECLINICALPHASE 1
 
  • Undisclosed Programs

Multiple Areas

SOURCE

https://www.schrodinger.com/pipeline/#Proprietary-Pipeline

 

In high-tech labs, workers are generating data to train A.I. algorithms to design better medicine, faster. But the transformation is just getting underway.

Terray Therapeutics campus in Monrovia, Calif., June 17, 2024

https://www.nytimes.com/2024/06/17/business/ai-drugs-development-terray.html?smid=nytcore-ios-share&referringSource=articleShare

 

Five AI drug discovery companies you should know about

  1. Atomwise 
  2. Cradle 
  3. Exscientia 
  4. Iktos 
  5. Insilico Medicine 

According to Grand View Research, the global AI in drug discovery market size was valued at $1.1 billion in 2022, and is expected to expand at a compound annual growth rate (CAGR) of 29.6% from 2023 to 2030. The report states that the growing demand for the discovery and development of novel drug therapies and increasing manufacturing capacities of the life science industry are driving the demand for AI-empowered solutions in the drug discovery processes. 

As this report suggests, AI for drug discovery is clearly a growing field within the biopharma industry. Inevitably, as it grows even larger, we will see more companies come to the forefront of the field, hoping to change the face of drug discovery – and also the biopharma industry as a whole – so that the entire drug development process can become faster, more consistent, more accurate, and more scalable.

SOURCE

https://www.labiotech.eu/best-biotech/ai-drug-discovery-companies/#:~:text=to%20%2433%20million.-,Exscientia,manner%20using%20its%20AI%20technology.

 

At LPBI Group, Of Note is our Journal PharmaceuticalIntelligence.com

it represents our commitment to AI technologies in the following research categories and How many articles have been written in each of these topics: 

  • A total of x articles have been categorized 511 times among the following Artificial Intelligence research categories

Artificial Intelligence – General

113

An executive’s guide to AI

9

Artificial Intelligence – Breakthroughs in Theories and Technologies

94

Artificial Intelligence Applications in Health Care

81

Artificial Intelligence in CANCER

29

Artificial Intelligence in Health Care – Tools & Innovations

55

Artificial Intelligence in Medicine – Application for Diagnosis

44

Artificial intelligence applications for cardiology

21

AI-assisted Cardiac MRI

9

Artificial Intelligence in Psychiatry

5

Artificial Intelligence in Medicine – Applications in Therapeutics

50

 

LPBI Group’s involvement in Conceptual Drug Development covers the following two areas:

DrugDiscovery @LPBI Group, 2016 – 2018

Synthetic Biology in Drug Discovery, 2021 – Present

 

Applications of Artificial Intelligence to Medicine

Artificial Intelligence: Genomics & Cancer, 2021 – Present

Medicine with GPT-4 & ChatGPT, 2023 – Present

 

LPBI Group commitment to Medical Text Analysis using Machine Learning

2021-2025 Medical Text Analysis (NLP), 2020 – Present

ChatGPT + Wolfram PlugIn, 2023 – Present

 

LPBI Group Team members published two books on Drug Delivery Technologies

We had covered drug delivery technologies in two of our books. See all the Books: 

https://www.amazon.com/s?k=Aviva+Lev-Ari&i=digital-text&rh=n%3A133140011&ref=nb_sb_noss

 

  • Series E, Volume Four 

Medical 3D BioPrinting – The Revolution in Medicine, Technologies for Patient-centered Medicine: From R&D in Biologics to New Medical Devices. 

https://www.amazon.com/dp/B078QVDV2W

and

  • Series C, Volume Two 

Cancer Therapies: Metabolic, Genomics, Interventional, Immunotherapy and Nanotechnology in Therapy Delivery (Series C Book 2).

http://www.amazon.com/dp/B071VQ6YYK

 

The Table of Contents of these two book can be found in our

Spanish-language Edition, as well

  • Serie E, Volumen 4

Bioimpresión médica en 3D: la revolución de la medicina: Tecnologías para una medicina centrada en el paciente: de la I+D en agentes biológicos a los nuevos … en el paciente nº 4) (Spanish Edition) 2023

(Spanish Edition) Kindle Edition

https://www.amazon.com/dp/B0BRNVDB1P $56

 

  • Serie C, Volumen 2

Tratamientos contra el cáncer: Metabólicos, genómicos, intervencionistas, inmunoterapia y nanotecnología para la administración de tratamientos (Serie … y la oncología nº 2) 2022

(Spanish Edition) Kindle Edition

http://www.amazon.com/dp/B0BQTM44SM $75

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Live Notes from JP Morgan Healthcare Conference Virtual Endpoints Preview: January 8-9 2024

Reporter: Stephen J. Williams, Ph.D.

Endpoints at #JPM24 | Primed to unlock biopharma’s next dealmaking wave
Endpoints at JP Morgan Healthcare Conference
January 8-9 | San Francisco, CA80 Mission St, San Francisco, CA

An oasis has emerged in the biopharma money desert as backers look to replenish capital — still, uncertainty remains on whether it’s a mirage or the much needed dealmaking bump the industry needs. Yet spirits run high as JPM24 marks the triumphant return of inking strategic alliances and peering into the industry crystal ball — while keeping an eye out for some major M&A.

We’re back live from San Francisco for JPM Monday and Tuesday — our calendar of can’t-miss panels and fireside chats will feature prominent biopharma leaders to watch. The Endpoints Hub provides the ultimate coworking space with everything you need — 1:1 and group meeting spots plus guest pass capabilities and more. Join us in-person at the Endpoints Hub or watch online to stay plugged into all the action.

8 JAN
Welcome remarks
8:05 AM – 8:25 AM PST
Pfizer vet Mikael Dolsten has some thoughts on Big Pharma R&D

Endpoints News founding editor John Carroll will sit down with longtime Pfizer CSO Mikael Dolsten to talk about Pfizer’s pipeline, what he’s learned on the job about preclinical research and development and what’s ahead for the pharma giant in drug development and deals.

Mikael Dolsten

Chief Scientific Officer, President, Pfizer Research & Development

Pfizer

Pfizer Mikael Dolsten: Pfizer produced a series of AI generated molecules with new properties. Sees rapid adoption of AI in the area of drug discovery and molecular design.

 
 
8:25 AM – 9:05 AM PST
What pharma wants: The industry’s dealmakers look ahead at 2024

The drug industry’s appetite for new assets hasn’t slowed down. Top business development execs will give their outlook on the year, what they’re looking for and how they see the market.

Glenn Hunzinger

Pharmaceutical & Life Sciences Consulting Solutions Leader

PwC US

Rachna Khosla

SVP, Head of Business Development

Amgen

James Sabry

Global Head of Pharma Partnering

Roche

Devang Bhuva

SVP, Corporate Development

Gilead Sciences, Inc.

Endpoints News

Dealmaking panel

Glenn Hunzinger: if you do not have a GLP1 will have a tough time getting a good market price for your company; capital markets are not where they want to be; sees a tough deal making climate like last year.  The problem with many biotech companies are they are coming earlier to the venture capital because of greater funding needs and so it is imperative that they articulate the potential of their company in scientific detail

Rachna Khosla:  Make sure your investors are not just CAPITAL PARTNERS but use their expertise and involve them in development issues you may have, especially ones that a young firm will face.  The problem is most investments assume what the future looks like (for example how antibody drug conjugates, once a field left for dead, has been rejuvenated because of advances in chemistry). 

James Sabry: noted that cardiac and metabolic drugs are now at the focus of many investors, especially with the new anti-obesity drugs on market

Devang Bhuva: Most deals we see start as collaborations or partnerships.  You want to involve an alliance management team early in the deal making process.  This process could take years.

 
9:05 AM – 9:20 AM PST
The IPO: How Apogee Therapeutics went public in the most challenging market in years

Not many biotechs went public in 2023. And of those that did, not many have had a great time of it. Apogee is the exception and our panel will offer a behind-the-scenes look at their decision to enter the market and what life is like as a young public company.

Michael Henderson

CEO

Apogee Therapeutics

Kyle LaHucik

MODERATOR

Senior Reporter

Endpoints News

Michael Henderson:  Not many biotech IPOs deals happened in 2023.  Michael feels it is because too many biotechs focused on building platforms, which was a hard sell in 2023.  He felt not many biotechs had clear milestones and investors wanted a clear primary validated target.  He said many biotech startups are in a funding crunch and most need at least $440M on their balance sheet to get to 2026.

9:50 AM – 10:10 AM PST
Top predictions for biotech in 2024

Catalent CEO Alessandro Maselli will be back at the big JPM healthcare confab to talk with Endpoints News founder John Carroll about their top predictions of what’s coming up for the biotech industry in 2024. The stakes couldn’t be higher as the industry grapples with headwinds and new opportunities in a gale of market forces. Two top observers share their thoughts on the year ahead.

Alessandro Maselli

President & CEO

Catalent

10:15 AM – 10:35 AM PST
Innovation at a crossroads: Keys to unlocking the value of science and technology

The industry has long discussed the promise of technology and the acceleration it provides in scientific advancement and across the industry value chain. However, the promise of its impact has yet to fully be realized. This discussion will outline the keys to unleashing this promise and the implications and actions to be taken by the biopharmaceutical companies across the industry.

Ray Pressburger

North America Life Sciences Industry Lead & Global Life Sciences Strategy Lead

Accenture

SPONSORED BY

10:35 AM – 11:05 AM PST
Activism and Investing: In conversation with Elliott Investment Management’s Marc Steinberg

Elliott has been behind many of 2023’s highest-profile healthcare investments, including multiple activist engagements and taking Syneos Health private. What has made large healthcare companies such interesting investment opportunities for firms like Elliott? What’s Elliott’s investing strategy in healthcare? And what should companies expect when an activist calls?

Marc Steinberg

Senior Portfolio Manager

Elliott Investment Management

Andrew Dunn

MODERATOR

Biopharma Correspondent

Endpoints News

11:05 AM – 11:35 AM PST
Creating ROI from AI

AI is predicted to transform the way drugs are made, from discovery to clinical trials to market. But beyond the initial hype and early adoption, where has AI made meaningful contributions to R&D? How does it help drug developers advance science? Endpoints publisher Arsalan Arif is convening a panel of leading experts to discuss the state of AI in the pharmaceutical landscape and the outlook for 2024. How does AI impact the drug pipeline, from the early steps of discovery to reducing trial failure rate?

Thomas Clozel

Co-Founder & CEO

Owkin

Venkat Sethuraman

SVP, Global Biometrics & Data Sciences

Bristol Myers Squibb

Frank O. Nestle

Global Head of Research & Chief Scientific Officer

Sanofi

Matthias Evers

Chief Business Officer

Evotec

Arsalan Arif

MODERATOR

Founder & Publisher

Endpoints News

SPONSORED BY

11:35 AM – 12:00 PM PST
Biopharma’s dealmaker: Behind the scenes with Centerview Partners co-president Eric Tokat

Almost every major biopharma deal in 2023 had Centerview’s name attached to it. And much of the time, Eric Tokat was the banker making those deals happen. Hear his outlook for 2024, how transactions are getting done and what’s placed his firm at the center of so much action.

E. Eric Tokat

Co-President, Investment Banking

Centerview Partners

CenterView Partners Eric Tokat feels dealmaking will improve in 2024, given the recent flurry of dealmaking at end of last year and right before main JPM Healthcare Conference.  He says Centerview wants to help the biotechs they invest in on their strategic path.  This may translate into buyers more actively involved (more than startups want) and buyers now are in the drivers seat as far as the timeline of deals and development.

Is the megamerger dead for this year?  He says it is very hard to see two major mergers happening but there will be many smaller and mid size biotech deals happening, but these deals will be more speculative in nature..  The focus for large pharma is top line growth.  Most of the buyers have an infrastructure and value is more of buying and dropping it in their business so there is now a huge emphasis on due diligence on whether synergies exist or not

 
12:00 PM – 12:30 PM PST
Founder, legend, leader: In conversation with Nobel laureate Carolyn Bertozzi

Carolyn Bertozzi’s discoveries around bioorthogonal chemistry won the Nobel Prize in Chemistry in 2022 and are at the heart of new therapies being tested in patients. Join us as we discuss what inspires her and where she sees the next big advances.

Carolyn Bertozzi

Prof. of Chemistry, Stanford University and Baker Family Director of Sarafan ChEM-H

Stanford University

Nicole DeFeudis

MODERATOR

Editor

Endpoints News

Bioorthogonal chemistry: class of high yielding chemical reactions that proceed rapidly and selectively in biological environments without side reactions toward endogenous functions.  This is also a type of ‘click chemistry’ in biological system where only specifically alter the biomolecule of interest.

Orthogonal: two chemicals not interacting with each other

Dr. Bertozzi noted she has started a new Antibody-Drug-Conjugate (ADC) company which involves designing with biorthogonal chemistry to make new functional molecules with varying properties

She noted hardly any biologists knew anything about glycobiology when she first started.  However now she feels pharma and academia are working very well with each other

Bioorthogonal and Click Chemistry
Curated by Prof. Carolyn R. Bertozzi, 2022 winner of the Nobel Prize in Chemistry

Source: https://pubs.acs.org/page/vi/bioorthogonal-click-chemistry

The 2022 Nobel Prize in Chemistry has been awarded jointly to ACS Central Science Editor-in-Chief, Carolyn R. Bertozzi of Stanford University, Morten Meldal of the University of Copenhagen, and K. Barry Sharpless of Scripps Research, for the development of click chemistry and bioorthogonal chemistry.

To celebrate this remarkable achievement, 2022 Nobel Prize winner Professor Carolyn R. Bertozzi has curated this Bioorthogonal and Click Chemistry Virtual Issue, highlighting papers published across ACS journals that have built upon the foundational work in this exciting area of chemistry.

From Mechanism to Mouse: A Tale of Two Bioorthogonal Reactions

Ellen M. Sletten and Carolyn R. Bertozzi* Acc. Chem. Res. 2011, 44, 9, 666-676 August 15, 2011

Abstract

Bioorthogonal reactions are chemical reactions that neither interact with nor interfere with a biological system. The participating functional groups must be inert to biological moieties, must selectively reactive with each other under biocompatible conditions, and, for in vivo applications, must be nontoxic to cells and organisms. Additionally, it is helpful if one reactive group is small and therefore minimally perturbing of a biomolecule into which it has been introduced either chemically or biosynthetically. Examples from the past decade suggest that a promising strategy for bioorthogonal reaction development begins with an analysis of functional group and reactivity space outside those defined by nature. Issues such as stability of reactants and products (particularly in water), kinetics, and unwanted side reactivity with biofunctionalities must be addressed, ideally guided by detailed mechanistic studies. Finally, the reaction must be tested in a variety of environments, escalating from aqueous media to biomolecule solutions to cultured cells and, for the most optimized transformations, to live organisms.

9 JAN

9:40 AM – 10:10 AM PST

Biotech downturn survival school

Our panelists have seen the worst, and made it through to the other side. Join us for downturn survival school as our panelists talk about what sets apart the ones who make it through tough times.

These panalists think it will be specialist capital year to shine while the general capital is still sitting on the sidelines

JJ Kang

CEO

Appia Bio

“2023 was a tough year while 2020 was a boon year to start a company.  We will continue to see these cycles; many of these new CEOs have never seen a biotech downturn yet and may not know how to preserve capital for the downturn”.

“Doing a partnership with Kite Pharmaceuticals early in our startp allowed us to get work done without risking a lot of capital, even if it means equity and asset dilution.  That makes sense. However even if you are small insist on being an equal partner.”

“There are many investors we talk to who do not want to invest in cell therapy.  Too risky now”

Carl Gordon

Managing Partner

OrbiMed Advisors

There are many macroeconomic factors affecting investment and capital today which will carry on through 2024.   Not raising money when you do not need money is a bad philosophy.  Always bbe raising captial.  This is especially true when you have to rely on hedge funds.  Parnerships howeve are sometimes the only way for small biotechs to leverage their strengths.

Joshua Boger

Executive Chair

Alkeus Pharmaceuticals, Inc.

Boger: Expect volatility for 2024.  This environment feels very different than past downturns.

Even in downturns there is still lots of capital; remember access to human capital is better in a downturn and is easier to access;  however it has become harder to get drug approvals

The panelists agree that access to capital and funding will be as tricky in 2024 than 2023.  They did

suggest that a new funding avenue, private credit, may be a source of capital.  This is discussed below:

When thinking about a private alternative investment asset class, the first thing that springs to mind is private equity. But there’s one more asset class with the word private in its name that has recently gained much attention. We’re talking about private credit

Indeed, this once little-known investment strategy is now growing rapidly in popularity, offering private investors worldwide an exciting opportunity to diversify their portfolio with, in theory, less risky investments that yield significant returns. 

  • Private credit investments refer to investors lending money to companies who then repay the loan at a given interest rate within the predetermined period.
  • The private credit market has grown significantly over the past years, rising from $875 million in 2020 to $1.4 trillion at the beginning of 2023. 

Please WATCH VIDEO BY GOLDMAN SACHS ON PRIVATE CREDIT

 

 

 

 

10:50 AM – 11:20 AM PST

The New Molecule: How breakthrough technologies are actually changing pharma R&D

Join us for a look at how AI, machine learning and generative technologies are actually being applied inside drugmakers’ labs. We’ll explore how new technologies are being used, their implications, how they intersect with regulatory and IP issues and how this fast-changing field is likely to evolve.

Kailash Swarna

Managing Director & Global Life Sciences Clinical Development Lead

Accenture

Artificial Intelligence is making impact in a grand way on biology in three aspects:

  1. Speeding up target validation: now we can get through 300 molecules a day
  2. Predicition like AlphaFold is doing; molecular simulations
  3. Document submission especially with regulatory and IND submissions

Pamela Carroll

COO

Isomorphic Labs formerly of AlphaFold

We were first with Novartis at last year JPM and was one year old but parnering with them in that initial year was very important for sealing the deal.

They are looking now at neurologic diseases like ALS.  She wondered whether ALS is actually multiple diseases and we need to stratify patients like we do in oncology trials.  Their main competion is the whole tech world like Amazon, Google and other Machine Learning companies so being a tech player in the biotech world means you are not just competing with other biotechs but large tech companies as well.

Jorge Conde

General Partner

Andreessen Horowitz

Need is still great for drug discovery; early adopters show AI tools can be used in big pharma. There are lots of applications of AI in managing care; a lot of back office applications including patient triaging.  He does not see big AI mergers with pharma companies –  this will be mainly partnerships not M&A deals

Alicyn Campbell

Chief Scientific Officer

Evinova, a Healthtech Subsidiary of the AstraZeneca Group

There is a need to turn AI for real world example.  For example AI tools were used in clinical trials to determine patient cohorts with pneumonitis.  At Evinova they are determining how AI can hel[p show clinical benefit with respect to efficacy and safety

Joshua Boger at #JPM24 (Brian Benton Photography)

  January 12, 2024 09:06 AM ESTUpdated 10:00 AM PeopleStartups

Vertex founder Joshua Boger on surviving downturns, ‘painful’ partnerships, and the importance of culture: #JPM24

Andrew Dunn

Biopharma Correspondent

Source: https://endpts.com/jpm24-vertex-founder-joshua-boger-on-surviving-downturns-painful-partnerships-and-the-importance-of-culture/

While the JP Morgan Healthcare Conference was full of voices of measured optimism, rooting for the market to bounce back in 2024, one longtime biotech leader warned against setting any firm expectations.

Instead of predicting when the downturn may end, Vertex Pharmaceuticals founder Joshua Boger said he advises biotech leaders to expect — and plan for — volatility. Speaking Tuesday on an Endpoints News panel alongside OrbiMed’s Carl Gordon and Appia Bio CEO JJ Kang, Boger shared lessons learned on surviving downturns, striking pharma deals, and the importance of keeping a company’s culture based on his two decades of founding and leading Vertex as CEO from 1989 to 2009. The 72-year-old is now serving as executive chairman of Alkeus Pharmaceuticals, a startup developing a rare disease drug.

“I never experienced a straight line up,” Boger said. “Everything had its cycles, and it was how you respond to the cycle, not by predicting when the end is going to be, but just by responding to the present situation.”

At Boger’s first appearance at the JP Morgan conference in 1991, he said the conference’s theme was the end of biotech financing. Just a few months later, Regeneron successfully went public, rapidly changing the outlook for the whole field.

“We had no idea we were ever going to take public money,” he said. “When Regeneron did their IPO, we went, ‘Whoa, there’s something happening here,’ and we pivoted quickly.”

Vertex went public later that year. Throughout his 20-year tenure, Boger said no pharma company ever made an acquisition offer for Vertex, which now commands a market value of $110 billion and recently won the first FDA approval for a CRISPR gene editing therapy.

“We had an uber corporate policy to always make ourselves more expensive than anyone would stomach,” Boger said.

However, Vertex did strike a range of partnerships with Big Pharmas, which Boger described as a painful but necessary part of running a biotech startup.

“It’s impossible for a partnership not to slow you down,” he said. “You can and should try as hard as you can not to do that, but just count on it. They’ll slow you down.”

Boger said startups should insist on being equal partners in pharma deals, at least making sure they have a seat at a partner’s development meetings.

“Realize they’re going to be painful, it’s going to be horrible, and you need to do it,” Boger said.

While Vertex suffered through layoffs, stock price plunges, and trial failures, Boger credited a focus on culture as key to its long-term success.

“It’s the most important ingredient for a successful company,” he said. “Technology is acquirable. Culture is not acquirable. There are 10 companies that will fail because of culture for every one that succeeds, and the successful companies in retrospect will almost always have special cultural aspects that kept them through those downtimes.”

JPM24 opens with ADCs the hottest ticket in San Francisco

By Annalee ArmstrongJan 8, 2024 6:30am

Source: https://www.fiercebiotech.com/biotech/jpm24-opens-adcs-hottest-ticket-san-francisco

The overall deal flow in biopharma tapered off in 2023 but the big companies sure know what they want (what they really, really want), according to a new report from J.P. Morgan.

And that’s antibody-drug conjugates, which drove a fourth-quarter spike in licensing deal proceeds and provided a glimmer of hope to an industry battered by outside forces and grim financing prospects.

J.P. Morgan’s annual 2023 Biopharma Licensing and Venture Report arrived on the eve of the firm’s famous conference, which is set to welcome thousands of attendees in San Francisco today—East Coast weather permitting.

2023 was tough, but clinical biotechs still had a lot of opportunities to wheel and deal, according to J.P. Morgan. While licensing deals, venture investments, M&A and IPOs were down overall in the fourth quarter, deal values stayed fairly high thanks to a flurry of late-stage tie ups.

Follow the Fierce team’s coverage of the 2024 J.P. Morgan Healthcare Conference here

Biopharma licensing partnerships accounted for $63 billion in total value during the fourth quarter from 108 deals. Just one deal—Merck’s ADC partnership with Daiichi Sankyo—accounted for $22 billion of that. Another huge one was another ADC bet, with Bristol Myers Squibb signing on to work with SystImmune for a total value of $8.4 billion. If you exclude the Merck deal, the total value of these partnerships is still higher than the previous quarter, which ended with $32.1 billion.

The total number of licensing deals compares to 149 in the same quarter a year earlier, 195 for Q4 2021 and 223 for Q4 2022.

As for venture investments, the year closed out with $17 billion total across 250 rounds, thanks to $3.5 billion earned through 79 rounds in the last quarter. Aiolos Bio snagged the title of largest venture round of the quarter with $245 million, which also proved to be the largest series A, too.

There was just one IPO in all of the fourth quarter—Cargo Therapeutics making the plunge for $300 million—and 13 overall for the year. It’s a far cry from the heyday of 2021 and experts are still unsure what 2024 will hold. J.P. Morgan reported $2.5 billion raised from 12 completed biopharma IPOs for the year on Nasdaq and NYSE. Nine out of the 12 companies had clinical programs when they took the leap to the public markets. As of December 13, five of the companies were trading above their IPO price.

As for M&A, December saw a rush of Big Pharmas snapping up companies around Christmas. J.P. Morgan tallied the fourth quarter at $37.6 billion and $128.8 billion across 112 total acquisitions for all of 2023.

AbbVie was the top buyer of the quarter with the two largest acquisitions thanks to the $10 billion outlay for ImmunoGen and $8.7 billion buy of Cerevel Therapeutics.

All of this adds up to 270 total deals in the fourth quarter total, which is lower than the third quarter which exceeded 300.

J.P. Morgan sees some big potential for smaller biopharmas looking for licensing partners, as Big Pharmas have been handing out larger upfront payments for the deals they really want.

Cancer was once again the most in-demand therapeutic areas, reaching a new height of $86.1 billion in 2023. Followed by $21.1 billion for neurological disorders.

For More Articles on Real Time Conference Coverage in this Open Access Scientific Journal see:

Part One: The Process of Real Time Coverage using Social Media

Part Two: List of BioTech Conferences 2013 to Present

https://worldmedicalinnovation.org/

https://pharmaceuticalintelligence.com/2022/05/01/2022-world-medical-innovation-forum-gene-cell-therapy-may-2-4-2022-boston-in-person/

 

https://event.technologyreview.com/emtech-digital-2022/agenda-overview

 

Read Full Post »

The Use of ChatGPT in the World of BioInformatics and Cancer Research and Development of BioGPT by MIT

Curator: Stephen J. Williams, Ph.D.

Chatbots are being used in multiple interdisciplanary areas in research and medicine and so it was a natural progression to incorporate artificial intelligence (AI), natural language processing (NLP), and chatbot technology like ChatGPT into bioinformatic analysis.

“In domains like informatics, management and marketing, media and communication science, languages and philosophy, psychology and sociology, engineering, design, and human-computer interaction, the fast expanding body of chatbot study is clearly interdisciplinary.

The field of bioinformatics is a natural fit for incorporting this technology.  The curated information below shows some use examples of this technology in bioinformatics related to cancer research.

The text in this reference source was actually generated by ChatGPT (https://chat.openai.com/chat)

Source: https://omicstutorials.com/using-chatgpt-in-bioinformatics-and-biomedical-research/

How ChatGPT can be used in bioinformatics research?
ChatGPT is a variant of the GPT (Generative Pre-trained Transformer) language model that was designed to generate human-like text in a conversational setting. It is not directly related to bioinformatics, which is the field of study that deals with the storage, retrieval, and analysis of biological data, particularly in the context of genetics and genomics. However, ChatGPT could potentially be used in a bioinformatics context as a tool for generating natural language descriptions of genetic or genomic data, or for generating responses to queries about such data.

For example, a researcher could use ChatGPT to generate descriptions of genetic variants or gene expression patterns based on input data. This could be useful for generating summaries of results for reports or papers, or for generating explanations of complex concepts for non-expert audiences. ChatGPT could also be used to generate responses to queries about genetic or genomic data, such as “What is the function of gene X?” or “What are the potential consequences of variant Y?”

It’s worth noting that ChatGPT is just one of many tools and techniques that can be used in bioinformatics, and it is unlikely to be the most suitable or effective option in every situation. There are many specialized tools and resources available for working with biological data, and it is often necessary to use a combination of these tools to fully analyze and understand the data.

Generating descriptions of genetic or genomic data: ChatGPT could be used to generate natural language descriptions of genetic or genomic data based on input data. For example, suppose a researcher has a dataset containing information about gene expression levels in different tissues. The researcher could use ChatGPT to generate a description of the data, such as:
“Gene X is highly expressed in the liver and kidney, with moderate expression in the brain and heart. Gene Y, on the other hand, shows low expression in all tissues except for the lung, where it is highly expressed.”

 

Thereby ChatGPT, at its simplest level, could be used to ask general questions like “What is the function of gene product X?” and a ChatGPT could give a reasonable response without the scientist having to browse through even highly curated databases lie GeneCards or UniProt or GenBank.  Or even “What are potential interactors of Gene X, validated by yeast two hybrid?” without even going to the curated InterActome databases or using expensive software like Genie.

Summarizing results: ChatGPT could be used to generate summaries of results from genetic or genomic studies. For example, a researcher might use ChatGPT to generate a summary of a study that found a association between a particular genetic variant and a particular disease. The summary might look something like this:
“Our study found that individuals with the variant form of gene X are more likely to develop disease Y. Further analysis revealed that this variant is associated with changes in gene expression that may contribute to the development of the disease.”

It’s worth noting that ChatGPT is just one tool that could potentially be used in these types of applications, and it is likely to be most effective when used in combination with other bioinformatics tools and resources. For example, a researcher might use ChatGPT to generate a summary of results, but would also need to use other tools to analyze the data and confirm the findings.

ChatGPT is a variant of the GPT (Generative Pre-training Transformer) language model that is designed for open-domain conversation. It is not specifically designed for generating descriptions of genetic variants or gene expression patterns, but it can potentially be used for this purpose if you provide it with a sufficient amount of relevant training data and fine-tune it appropriately.

To use ChatGPT to generate descriptions of genetic variants or gene expression patterns, you would first need to obtain a large dataset of examples of descriptions of genetic variants or gene expression patterns. You could use this dataset to fine-tune the ChatGPT model on the task of generating descriptions of genetic variants or gene expression patterns.

Here’s an example of how you might use ChatGPT to generate a description of a genetic variant:

First, you would need to pre-process your dataset of descriptions of genetic variants to prepare it for use with ChatGPT. This might involve splitting the descriptions into individual sentences or phrases, and encoding them using a suitable natural language processing (NLP) library or tool.

Next, you would need to fine-tune the ChatGPT model on the task of generating descriptions of genetic variants. This could involve using a tool like Hugging Face’s Transformers library to load the ChatGPT model and your pre-processed dataset, and then training the model on the task of generating descriptions of genetic variants using an appropriate optimization algorithm.

Once the model has been fine-tuned, you can use it to generate descriptions of genetic variants by providing it with a prompt or seed text and asking it to generate a response. For example, you might provide the model with the prompt “Generate a description of a genetic variant associated with increased risk of breast cancer,” and ask it to generate a response. The model should then generate a description of a genetic variant that is associated with increased risk of breast cancer.

It’s worth noting that generating high-quality descriptions of genetic variants or gene expression patterns is a challenging task, and it may be difficult to achieve good results using a language model like ChatGPT without a large amount of relevant training data and careful fine-tuning.

 

To train a language model like chatGPT to extract information about specific genes or diseases from research papers, you would need to follow these steps:

Gather a large dataset of research papers that contain information about the specific genes or diseases you are interested in. This dataset should be diverse and representative of the types of papers you want the model to be able to extract information from.

Preprocess the text data in the research papers by tokenizing the text and creating a vocabulary. You may also want to consider lemmatizing or stemming the text to reduce the dimensionality of the dataset.

Train the language model on the preprocessed text data. You may want to fine-tune a pre-trained model such as chatGPT on your specific dataset, or you can train a new model from scratch.

ChatGPT could also be useful for sequence analysis

A few examples of sequence analysis a ChatGPT could be useful include:

  1. Protein structure
  2. Identifying functional regions of a protein
  3. Predicting protein-protein interactions
  4. Identifying protein homologs
  5. Generating Protein alignments

All this could be done without having access to UNIX servers or proprietary software or knowing GCG coding

ChatGPT in biomedical research
There are several potential ways that ChatGPT or other natural language processing (NLP) models could be applied in biomedical research:

Text summarization: ChatGPT or other NLP models could be used to summarize large amounts of text, such as research papers or clinical notes, in order to extract key information and insights more quickly.

Data extraction: ChatGPT or other NLP models could be used to extract structured data from unstructured text sources, such as research papers or clinical notes. For example, the model could be trained to extract information about specific genes or diseases from research papers, and then used to create a database of this information for further analysis.

Literature review: ChatGPT or other NLP models could be used to assist with literature review tasks, such as identifying relevant papers, extracting key information from papers, or summarizing the main findings of a group of papers.

Predictive modeling: ChatGPT or other NLP models could be used to build predictive models based on large amounts of text data, such as electronic health records or research papers. For example, the model could be trained to predict the likelihood of a patient developing a particular disease based on their medical history and other factors.

It’s worth noting that while NLP models like ChatGPT have the potential to be useful tools in biomedical research, they are only as good as the data they are trained on, and it is important to carefully evaluate the quality and reliability of any results generated by these models.

 

ChatGPT in text mining of biomedical data
ChatGPT could potentially be used for text mining in the biomedical field in a number of ways. Here are a few examples:

Extracting information from scientific papers: ChatGPT could be trained on a large dataset of scientific papers in the biomedical field, and then used to extract specific pieces of information from these papers, such as the names of compounds, their structures, and their potential uses.

Generating summaries of scientific papers: ChatGPT could be used to generate concise summaries of scientific papers in the biomedical field, highlighting the main findings and implications of the research.

Identifying trends and patterns in scientific literature: ChatGPT could be used to analyze large datasets of scientific papers in the biomedical field and identify trends and patterns in the data, such as emerging areas of research or common themes among different papers.

Generating questions for further research: ChatGPT could be used to suggest questions for further research in the biomedical field based on existing scientific literature, by identifying gaps in current knowledge or areas where further investigation is needed.

Generating hypotheses for scientific experiments: ChatGPT could be used to generate hypotheses for scientific experiments in the biomedical field based on existing scientific literature and data, by identifying potential relationships or associations that could be tested in future research.

 

PLEASE WATCH VIDEO

 

In this video, a bioinformatician describes the ways he uses ChatGPT to increase his productivity in writing bioinformatic code and conducting bioinformatic analyses.

He describes a series of uses of ChatGPT in his day to day work as a bioinformatian:

  1. Using ChatGPT as a search engine: He finds more useful and relevant search results than a standard Google or Yahoo search.  This saves time as one does not have to pour through multiple pages to find information.  However, a caveat is ChatGPT does NOT return sources, as highlighted in previous postings on this page.  This feature of ChatGPT is probably why Microsoft bought OpenAI in order to incorporate ChatGPT in their Bing search engine, as well as Office Suite programs

 

  1. ChatGPT to help with coding projects: Bioinformaticians will spend multiple hours searching for and altering open access available code in order to run certain function like determining the G/C content of DNA (although there are many UNIX based code that has already been established for these purposes). One can use ChatGPT to find such a code and then assist in debugging that code for any flaws

 

  1. ChatGPT to document and add coding comments: When writing code it is useful to add comments periodically to assist other users to determine how the code works and also how the program flow works as well, including returned variables.

 

One of the comments was interesting and directed one to use BIOGPT instead of ChatGPT

 

@tzvi7989

1 month ago (edited)

0:54 oh dear. You cannot use chatgpt like that in Bioinformatics as it is rn without double checking the info from it. You should be using biogpt instead for paper summarisation. ChatGPT goes for human-like responses over precise information recal. It is quite good for debugging though and automating boring awkward scripts

So what is BIOGPT?

BioGPT https://github.com/microsoft/BioGPT

 

The BioGPT model was proposed in BioGPT: generative pre-trained transformer for biomedical text generation and mining by Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon and Tie-Yan Liu. BioGPT is a domain-specific generative pre-trained Transformer language model for biomedical text generation and mining. BioGPT follows the Transformer language model backbone, and is pre-trained on 15M PubMed abstracts from scratch.

The abstract from the paper is the following:

Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks, respectively, and 78.2% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.

Tips:

  • BioGPT is a model with absolute position embeddings so it’s usually advised to pad the inputs on the right rather than the left.
  • BioGPT was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next token in a sequence. Leveraging this feature allows BioGPT to generate syntactically coherent text as it can be observed in the run_generation.py example script.
  • The model can take the past_key_values (for PyTorch) as input, which is the previously computed key/value attention pairs. Using this (past_key_values or past) value prevents the model from re-computing pre-computed values in the context of text generation. For PyTorch, see past_key_values argument of the BioGptForCausalLM.forward() method for more information on its usage.

This model was contributed by kamalkraj. The original code can be found here.

 

This repository contains the implementation of BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining, by Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon and Tie-Yan Liu. BioGPT is a github which is being developed by MIT in collaboration with Microsoft. It is based on Python.

License

BioGPT is MIT-licensed. The license applies to the pre-trained models as well.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

As of right now this does not seem Open Access, however a sign up is required!

We provide our pre-trained BioGPT model checkpoints along with fine-tuned checkpoints for downstream tasks, available both through URL download as well as through the Hugging Face 🤗 Hub.

Model Description URL 🤗 Hub
BioGPT Pre-trained BioGPT model checkpoint link link
BioGPT-Large Pre-trained BioGPT-Large model checkpoint link link
BioGPT-QA-PubMedQA-BioGPT Fine-tuned BioGPT for question answering task on PubMedQA link
BioGPT-QA-PubMedQA-BioGPT-Large Fine-tuned BioGPT-Large for question answering task on PubMedQA link
BioGPT-RE-BC5CDR Fine-tuned BioGPT for relation extraction task on BC5CDR link
BioGPT-RE-DDI Fine-tuned BioGPT for relation extraction task on DDI link
BioGPT-RE-DTI Fine-tuned BioGPT for relation extraction task on KD-DTI link
BioGPT-DC-HoC Fine-tuned BioGPT for document classification task on HoC link

 

So far as to date (6/12/2023) there are three papers referencing BioGPT on PubMed (abstracts and links below)

 

BioGPT: generative pre-trained transformer for biomedical text generation and mining.

Luo R, Sun L, Xia Y, Qin T, Zhang S, Poon H, Liu TY.Brief Bioinform. 2022 Nov 19;23(6):bbac409. doi: 10.1093/bib/bbac409.PMID: 36156661

We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. …Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fl …

Here they show the advantages of BioGPT over BioBERT and ChatGPT in an example of text extraction and question answer

Table 7. Example of generated text

Input                                           Model                 Text

Bicalutamide                               GPT-2                 Bicalutamide, a synthetic bicalutamide derivative, was found to increase the cellular proliferation

of C. elegans cells. This effect was not observed with the other synthetic bicalutamide derivatives. This is in line with our previous work which showed that bicalutamide increases the proliferation of C. elegans cells but not in the presence of synthetic bicalutamide.

BioGPT             Bicalutamide (Casodex) is an androgen receptor (AR) antagonist approved for the treatment of metastatic castration-resistant prostate cancer (mCRPC) in patients who have progressed on or are ineligible for docetaxel chemotherapy, as well as for the treatment of early-stage prostate cancer in men who have not progressed on or are ineligible for docetaxel chemotherapy, as well as for the treatment of metastatic castration-sensitive prostate cancer (mCSPC) in men who have not progressed on or are ineligible for docetaxel chemotherapy.

Janus kinase 3 (JAK-3)                 GPT-2               Janus kinase 3 (JAK-3) mediates the conversion of glucose to glycogen in skeletal muscle. The
increase in glucose uptake by skeletal muscle is believed to be associated with a reduction in muscle protein breakdown and with the release of ketone bodies, which contribute to the metabolism of glucose.

BioGPT             Janus kinase 3 (JAK-3) is a member of the Janus kinase (JAK) family of non-receptor tyrosine kinases and plays an important role in the regulation of cell proliferation, differentiation, survival, migration and angiogenesis.

Apricitabine                                 GPT-2               Apricitabine, duloxetine and citalopram) were investigated in patients with a history of
treatment-resistant depression (n = 10). We found that treatment with the three drugs significantly decreased depressive symptoms, but not depressive symptoms over the next 12 weeks. We conclude that antidepressant drugs that reduce depressive symptoms and reduce depressive symptoms over time may be beneficial in patients with recurrent depression.

BioGPT             Apricitabine is an oral prodrug of 5-aza-2 ’-deoxycytidine (5-aza-CdR), a DNA methyltransferase (DNMT) inhibitor, which has been approved by the US Food and Drug Administration (FDA) for the treatment of myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) in combination with low-dose cytarabine (Ara-C) and granulocyte colony-stimulating factor (G-CSF) for patients with intermediate-2 or high-risk MDS or AML.

Xylazine                                      GPT-2               Xylazine, the ‘active ingredient’ in ‘bath salts’ (see above) has been linked to numerous deaths,
including those of former University of California, Berkeley, professor Andrew Anglin, who posted

a video of himself having a bath filled with it.

BioGPT             Xylazine is an alpha 2-adrenoceptor agonist which has been used as a sedative and analgesic in veterinary medicine for many years, but its effects on the cardiovascular system have not been extensively studied in the dog, and its effects on the central nervous system (CNS) have not been well characterized in the dog, despite the fact that xylazine has been widely used as a sedative and analgesic in veterinary medicine for more than 30 years.

Psoralen                                      GPT-2               Psoralen, the city of the spiritualist, and had been given to him by God, who wished to show him
the way of salvation, and to draw him back to the true life of Christ, and to bring him into communion with his fellow-man.

BioGPT             Psoralen photochemotherapy (PUVA) is a well-established treatment for psoriasis, but its use is limited by the risk of skin cancer, particularly squamous cell carcinoma (SCC) of the head and neck (H & N), which is the most common site of skin cancer in the United States (US)

CP-673451                                  GPT-2                 CP-673451) and 4-5-6-3-1-2-3-4-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24-25-26-27-

28-29-30-31-32-33-34-35-36-37-38-39-40-41-42-43-44-45-46-47-48-49-50…

BioGPT             CP-673451 is a potent, selective, and orally active inhibitor of human neutrophil elastase (HNE) and human cathepsin G (CatG) with in vitro and in vivo anti-inflammatory activity in a variety of animal models of inflammation and in a model of acute lung injury (ALI) in the rat induced by intratracheal instillation of lipopolysaccharide (LPS) and tumor necrosis factor-alpha (TNF-alpha), a model of acute lung injury (ALI) in which neutrophils play an important role.

BIIB-021                                     GPT-2               BIIB-021A-2-1.pdf. Wright, C. (2002). ‘The War on Terror: A Legal Theory of Terrorist Attacks,’ in
L.E.R. Wright, S.R. Warrick and J.H. Rothman (Eds.), The War on Terror: Essays in Honor of Michael T. Klare (New York: Oxford University Press), 143-57.

BioGPT             BIIB-021 is a novel, orally active, non-peptide bradykinin B2 receptor antagonist with potent and long-lasting anti-inflammatory activity in animal models of acute and chronic inflammation and in a rat model of adjuvant-induced arthritis (AIA), an animal model of rheumatoid arthritis (RA) and in a rat model of collagen-induced arthritis (CIA), an animal model of collagen-induced arthritis (CIA), in which arthritis is induced by immunization with bovine type II collagen (CII).

Note how BioGPT is more descriptive and accurate!

EGFI: drug-drug interaction extraction and generation with fusion of enriched entity and sentence information.

Huang L, Lin J, Li X, Song L, Zheng Z, Wong KC.Brief Bioinform. 2022 Jan 17;23(1):bbab451. doi: 10.1093/bib/bbab451.PMID: 34791012

The rapid growth in literature accumulates diverse and yet comprehensive biomedical knowledge hidden to be mined such as drug interactions. However, it is difficult to extract the heterogeneous knowledge to retrieve or even discover the latest and novel knowledge in an efficient manner. To address such a problem, we propose EGFI for extracting and consolidating drug interactions from large-scale medical literature text data. Specifically, EGFI consists of two parts: classification and generation. In the classification part, EGFI encompasses the language model BioBERT which has been comprehensively pretrained on biomedical corpus. In particular, we propose the multihead self-attention mechanism and packed BiGRU to fuse multiple semantic information for rigorous context modeling. In the generation part, EGFI utilizes another pretrained language model BioGPT-2 where the generation sentences are selected based on filtering rules.

Results: We evaluated the classification part on ‘DDIs 2013’ dataset and ‘DTIs’ dataset, achieving the F1 scores of 0.842 and 0.720 respectively. Moreover, we applied the classification part to distinguish high-quality generated sentences and verified with the existing growth truth to confirm the filtered sentences. The generated sentences that are not recorded in DrugBank and DDIs 2013 dataset demonstrated the potential of EGFI to identify novel drug relationships.

Availability: Source code are publicly available at https://github.com/Layne-Huang/EGFI.

 

GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information.

Jin Q, Yang Y, Chen Q, Lu Z.ArXiv. 2023 May 16:arXiv:2304.09667v3. Preprint.PMID: 37131884 Free PMC article.

While large language models (LLMs) have been successfully applied to various tasks, they still face challenges with hallucinations. Augmenting LLMs with domain-specific tools such as database utilities can facilitate easier and more precise access to specialized knowledge. In this paper, we present GeneGPT, a novel method for teaching LLMs to use the Web APIs of the National Center for Biotechnology Information (NCBI) for answering genomics questions. Specifically, we prompt Codex to solve the GeneTuring tests with NCBI Web APIs by in-context learning and an augmented decoding algorithm that can detect and execute API calls. Experimental results show that GeneGPT achieves state-of-the-art performance on eight tasks in the GeneTuring benchmark with an average score of 0.83, largely surpassing retrieval-augmented LLMs such as the new Bing (0.44), biomedical LLMs such as BioMedLM (0.08) and BioGPT (0.04), as well as GPT-3 (0.16) and ChatGPT (0.12). Our further analyses suggest that: (1) API demonstrations have good cross-task generalizability and are more useful than documentations for in-context learning; (2) GeneGPT can generalize to longer chains of API calls and answer multi-hop questions in GeneHop, a novel dataset introduced in this work; (3) Different types of errors are enriched in different tasks, providing valuable insights for future improvements.

PLEASE WATCH THE FOLLOWING VIDEOS ON BIOGPT

This one entitled

Microsoft’s BioGPT Shows Promise as the Best Biomedical NLP

 

gives a good general description of this new MIT/Microsoft project and its usefullness in scanning 15 million articles on PubMed while returning ChatGPT like answers.

 

Please note one of the comments which is VERY IMPORTANT


@rufus9322

2 months ago

bioGPT is difficult for non-developers to use, and Microsoft researchers seem to default that all users are proficient in Python and ML.

 

Much like Microsoft Azure it seems this BioGPT is meant for developers who have advanced programming skill.  Seems odd then to be paying programmers multiK salaries when one or two Key Opinion Leaders from the medical field might suffice but I would be sure Microsoft will figure this out.

 

ALSO VIEW VIDEO

 

 

This is a talk from Microsoft on BioGPT

 

Other Relevant Articles on Natural Language Processing in BioInformatics, Healthcare and ChatGPT for Medicine on this Open Access Scientific Journal Include

Medicine with GPT-4 & ChatGPT
Explanation on “Results of Medical Text Analysis with Natural Language Processing (NLP) presented in LPBI Group’s NEW GENRE Edition: NLP” on Genomics content, standalone volume in Series B and NLP on Cancer content as Part B New Genre Volume 1 in Series C

Proposal for New e-Book Architecture: Bi-Lingual eTOCs, English & Spanish with NLP and Deep Learning results of Medical Text Analysis – Phase 1: six volumes

From High-Throughput Assay to Systems Biology: New Tools for Drug Discovery

Machine Learning (ML) in cancer prognosis prediction helps the researcher to identify multiple known as well as candidate cancer diver genes

 

20 articles in Natural Language Processing

142 articles in BioIT: BioInformatics

111 articles in BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics

 

Read Full Post »

Reporter: Frason Francis Kalapurakal, Research Assistant II

Researchers from MIT and Technion have made a significant contribution to the field of machine learning by developing an adaptive algorithm that addresses the challenge of determining when a machine should follow a teacher’s instructions or explore on its own. The algorithm autonomously decides whether to use imitation learning, which involves mimicking the behavior of a skilled teacher, or reinforcement learning, which relies on trial and error to learn from the environment.

The researchers’ key innovation lies in the algorithm’s adaptability and ability to determine the most effective learning method throughout the training process. To achieve this, they trained two “students” with different learning approaches: one using a combination of reinforcement and imitation learning, and the other relying solely on reinforcement learning. The algorithm continuously compared the performance of these two students, adjusting the emphasis on imitation or reinforcement learning based on which student achieved better results.

The algorithm’s efficacy was tested through simulated training scenarios, such as navigating mazes or reorienting objects with touch sensors. In all cases, the algorithm demonstrated superior performance compared to non-adaptive methods, achieving nearly perfect success rates and significantly outperforming other methods in terms of both accuracy and speed. This adaptability could enhance the training of machines in real-world situations where uncertainty is prevalent, such as robots navigating unfamiliar buildings or performing complex tasks involving object manipulation and locomotion.

Furthermore, the algorithm’s potential applications extend beyond robotics to various domains where imitation or reinforcement learning is employed. For example, large language models like GPT-4 could be used as teachers to train smaller models to excel in specific tasks. The researchers also suggest that analyzing the similarities and differences between machines and humans learning from their respective teachers could provide valuable insights for improving the learning experience.The MIT and Technion researchers’ algorithm stands out due to its principled approach, efficiency, and versatility across different domains. Unlike existing methods that require brute-force trial-and-error or manual tuning of parameters, their algorithm dynamically adjusts the balance between imitation and trial-and-error learning based on performance comparisons. This robustness, adaptability, and promising results make it a noteworthy advancement in the field of machine learning.

References:

“TGRL: TEACHER GUIDED REINFORCEMENT LEARNING ALGORITHM FOR POMDPS” Reincarnating Reinforcement Learning Workshop at ICLR 2023 https://openreview.net/pdf?id=kTqjkIvjj7

https://arxiv.org/abs/2301.01219

Reinforcement Learning: A Survey by L. P. Kaelbling, M. L. Littman, A. W. Moore https://doi.org/10.48550/arXiv.cs/9605103

Concrete Problems in AI Safety by Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, Dan Mané https://arxiv.org/abs/1606.06565

Other related articles published in this Open Access Online Scientific Journal include the following:

92 articles in the Category:

‘Artificial Intelligence – Breakthroughs in Theories and Technologies’ 

https://pharmaceuticalintelligence.com/category/artificial-intelligence-general/artificial-intelligence-breakthroughs-in-theories-and-technologies/

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Technion #1 in Europe in Field of AI for 2nd Straight Year

Reporter: Aviva Lev-Ari, PhD, RN

For the second year in a row, the Technion is ranked first in Europe in the field of artificial intelligence (AI) according to CSRankings, which are highly regarded for their metrics-based ranking of top computer science institutions. The repeat win further solidifies the Technion’s position as a leading institution in AI. It was also ranked 16th in the world in AI and 10th in the world in the subfield of learning systems. 

The Technion recruits researchers and students from all Technion units for interdisciplinary AI research by increasing the number of new programs and initiatives in its various fields with leading companies, top universities, and research institutions around the world. It is also establishing its own AI community to empower the student body and researchers in all fields of AI and deepening their collaborations with others doing related work.  

The Technion’s Tech.AI Center for Artificial Intelligence, established in 2020, is the greatest source of AI innovation and research on campus. Tech.AI includes approximately 150 researchers and aims to apply advanced methodologies and tools at the forefront of AI in a variety of fields including data science, medical research, mechanical engineering, civil engineering, architecture, biology, and more.  

To further facilitate AI research and collaborations, a recent agreement was signed to establish a Zimin Institute at the Technion for AI Solutions in Healthcare that will operate as part of Tech.AI. The Institute will promote interdisciplinary projects and work to develop technologies based on big data and computational learning in order to improve human health and healthcare, with an emphasis on proposals that have an applied AI component.  

https://ats.org/our-impact/technion-1-in-europe-in-field-of-ai-for-2nd-straight-year/?utm_medium=email&utm_source=ats-newsletter&utm_campaign=enews&s_src=enews

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Genomic data can predict miscarriage and IVF failure

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

Infertility is a major reproductive health issue that affects about 12% of women of reproductive age in the United States. Aneuploidy in eggs accounts for a significant proportion of early miscarriage and in vitro fertilization failure. Recent studies have shown that genetic variants in several genes affect chromosome segregation fidelity and predispose women to a higher incidence of egg aneuploidy. However, the exact genetic causes of aneuploid egg production remain unclear, making it difficult to diagnose infertility based on individual genetic variants in mother’s genome. Although, age is a predictive factor for aneuploidy, it is not a highly accurate gauge because aneuploidy rates within individuals of the same age can vary dramatically.

Researchers described a technique combining genomic sequencing with machine-learning methods to predict the possibility a woman will undergo a miscarriage because of egg aneuploidy—a term describing a human egg with an abnormal number of chromosomes. The scientists were able to examine genetic samples of patients using a technique called “whole exome sequencing,” which allowed researchers to home in on the protein coding sections of the vast human genome. Then they created software using machine learning, an aspect of artificial intelligence in which programs can learn and make predictions without following specific instructions. To do so, the researchers developed algorithms and statistical models that analyzed and drew inferences from patterns in the genetic data.

As a result, the scientists were able to create a specific risk score based on a woman’s genome. The scientists also identified three genes—MCM5, FGGY and DDX60L—that when mutated and are highly associated with a risk of producing eggs with aneuploidy. So, the report demonstrated that sequencing data can be mined to predict patients’ aneuploidy risk thus improving clinical diagnosis. The candidate genes and pathways that were identified in the present study are promising targets for future aneuploidy studies. Identifying genetic variations with more predictive power will serve women and their treating clinicians with better information.

References:

https://medicalxpress-com.cdn.ampproject.org/c/s/medicalxpress.com/news/2022-06-miscarriage-failure-vitro-fertilization-genomic.amp

https://pubmed.ncbi.nlm.nih.gov/35347416/

https://pubmed.ncbi.nlm.nih.gov/31552087/

https://pubmed.ncbi.nlm.nih.gov/33193747/

https://pubmed.ncbi.nlm.nih.gov/33197264/

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Data Science: Step by Step – A Resource for LPBI Group One-Year Internship in IT, IS, DS

Reporter: Aviva Lev-Ari, PhD, RN

9 free Harvard courses: learning Data Science

In this article, I will list 9 free Harvard courses that you can take to learn data science from scratch. Feel free to skip any of these courses if you already possess knowledge of that subject.

Step 1: Programming

The first step you should take when learning data science is to learn to code. You can choose to do this with your choice of programming language?—?ideally Python or R.

If you’d like to learn R, Harvard offers an introductory R course created specifically for data science learners, called Data Science: R Basics.

This program will take you through R concepts like variables, data types, vector arithmetic, and indexing. You will also learn to wrangle data with libraries like dplyr and create plots to visualize data.

If you prefer Python, you can choose to take CS50’s Introduction to Programming with Python offered for free by Harvard. In this course, you will learn concepts like functions, arguments, variables, data types, conditional statements, loops, objects, methods, and more.

Both programs above are self-paced. However, the Python course is more detailed than the R program, and requires a longer time commitment to complete. Also, the rest of the courses in this roadmap are taught in R, so it might be worth learning R to be able to follow along easily.

Step 2: Data Visualization

Visualization is one of the most powerful techniques with which you can translate your findings in data to another person.

With Harvard’s Data Visualization program, you will learn to build visualizations using the ggplot2 library in R, along with the principles of communicating data-driven insights.

Step 3: Probability

In this course, you will learn essential probability concepts that are fundamental to conducting statistical tests on data. The topics taught include random variables, independence, Monte Carlo simulations, expected values, standard errors, and the Central Limit Theorem.

The concepts above will be introduced with the help of a case study, which means that you will be able to apply everything you learned to an actual real-world dataset.

Step 4: Statistics

After learning probability, you can take this course to learn the fundamentals of statistical inference and modelling.
This program will teach you to define population estimates and margin of errors, introduce you to Bayesian statistics, and provide you with the fundamentals of predictive modeling.

Step 5: Productivity Tools (Optional)

I’ve included this project management course as optional since it isn’t directly related to learning data science. Rather, you will be taught to use Unix/Linux for file management, Github, version control, and creating reports in R.

The ability to do the above will save you a lot of time and help you better manage end-to-end data science projects.

Step 6: Data Pre-Processing

The next course in this list is called Data Wrangling, and will teach you to prepare data and convert it into a format that is easily digestible by machine learning models.

You will learn to import data into R, tidy data, process string data, parse HTML, work with date-time objects, and mine text.

As a data scientist, you often need to extract data that is publicly available on the Internet in the form of a PDF document, HTML webpage, or a Tweet. You will not always be presented with clean, formatted data in a CSV file or Excel sheet.

By the end of this course, you will learn to wrangle and clean data to come up with critical insights from it.

Step 7: Linear Regression

Linear regression is a machine learning technique that is used to model a linear relationship between two or more variables. It can also be used to identify and adjust the effect of confounding variables.

This course will teach you the theory behind linear regression models, how to examine the relationship between two variables, and how confounding variables can be detected and removed before building a machine learning algorithm.

Step 8: Machine Learning

Finally, the course you’ve probably been waiting for! Harvard’s machine learning program will teach you the basics of machine learning, techniques to mitigate overfitting, supervised and unsupervised modelling approaches, and recommendation systems.

Step 9: Capstone Project

After completing all the above courses, you can take Harvard’s data science capstone project, where your skills in data visualization, probability, statistics, data wrangling, data organization, regression, and machine learning will be assessed.

With this final project, you will get the opportunity to put together all the knowledge learnt from the above courses and gain the ability to complete a hands-on data science project from scratch.

Note: All the courses above are available on an online learning platform from edX and can be audited for free. If you want a course certificate, however, you will have to pay for one.

Building a data science learning roadmap with free courses offered by MIT.

8 Free MIT Courses to Learn Data Science Online

 enrolled into an undergraduate computer science program and decided to major in data science. I spent over $25K in tuition fees over the span of three years, only to graduate and realize that I wasn’t equipped with the skills necessary to land a job in the field.

I barely knew how to code, and was unclear about the most basic machine learning concepts.

I took some time out to try and learn data science myself — with the help of YouTube videos, online courses, and tutorials. I realized that all of this knowledge was publicly available on the Internet and could be accessed for free.

It came as a surprise that even Ivy League universities started making many of their courses accessible to students worldwide, for little to no charge. This meant that people like me could learn these skills from some of the best institutions in the world, instead of spending thousands of dollars on a subpar degree program.

In this article, I will provide you with a data science roadmap I created using only freely available MIT online courses.

Step 1: Learn to code

I highly recommend learning a programming language before going deep into the math and theory behind data science models. Once you learn to code, you will be able to work with real-world datasets and get a feel of how predictive algorithms function.

MIT Open Courseware offers a beginner-friendly Python program for beginners, called Introduction to Computer Science and Programming.

This course is designed to help people with no prior coding experience to write programs to tackle useful problems.

Step 2: Statistics

Statistics is at the core of every data science workflow — it is required when building a predictive model, analyzing trends in large amounts of data, or selecting useful features to feed into your model.

MIT Open Courseware offers a beginner-friendly course called Introduction to Probability and Statistics. After taking this course, you will learn the basic principles of statistical inference and probability. Some concepts covered include conditional probability, Bayes theorem, covariance, central limit theorem, resampling, and linear regression.

This course will also walk you through statistical analysis using the R programming language, which is useful as it adds on to your tool stack as a data scientist.

Another useful program offered by MIT for free is called Statistical Thinking and Data Analysis. This is another elementary course in the subject that will take you through different data analysis techniques in Excel, R, and Matlab.

You will learn about data collection, analysis, different types of sampling distributions, statistical inference, linear regression, multiple linear regression, and nonparametric statistical methods.

Step 3: Foundational Math Skills

Calculus and linear algebra are two other branches of math that are used in the field of machine learning. Taking a course or two in these subjects will give you a different perspective of how predictive models function, and the working behind the underlying algorithm.

To learn calculus, you can take Single Variable Calculus offered by MIT for free, followed by Multivariable Calculus.

Then, you can take this Linear Algebra class by Prof. Gilbert Strang to get a strong grasp of the subject.

All of the above courses are offered by MIT Open Courseware, and are paired with lecture notes, problem sets, exam questions, and solutions.

Step 4: Machine Learning

Finally, you can use the knowledge gained in the courses above to take MIT’s Introduction to Machine Learning course. This program will walk you through the implementation of predictive models in Python.

The core focus of this course is in supervised and reinforcement learning problems, and you will be taught concepts such as generalization and how overfitting can be mitigated. Apart from just working with structured datasets, you will also learn to process image and sequential data.

MIT’s machine learning program cites three pre-requisites — Python, linear algebra, and calculus, which is why it is advisable to take the courses above before starting this one.

Are These Courses Beginner-Friendly?

Even if you have no prior knowledge of programming, statistics, or mathematics, you can take all the courses listed above.

MIT has designed these programs to take you through the subject from scratch. However, unlike many MOOCs out there, the pace does build up pretty quickly and the courses cover a large depth of information.

Due to this, it is advisable to do all the exercises that come with the lectures and work through all the reading material provided.

SOURCE

Natassha Selvaraj is a self-taught data scientist with a passion for writing. You can connect with her on LinkedIn.

https://www.kdnuggets.com/2022/03/8-free-mit-courses-learn-data-science-online.html

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Tweet Collection of 2022 #EmTechDigital @MIT, March 29-30, 2022

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Reporter: Aviva Lev-Ari, PhD, RN

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2022 EmTechDigital @MIT, March 29-30, 2022

Real Time Coverage: Aviva Lev-Ari, PhD, RN 

#EmTechDigital

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SPEAKERS

https://event.technologyreview.com/emtech-digital-2022/speakers

Ali
Alvi

Turing Group Program Manager

Microsoft

Refik
Anadol

CEO, RAS Lab; Lecturer

UCLA

Lauren
Bennett

Group Software Engineering Lead, Spatial Analysis and Data Science

Esri

Elizabeth
Bramson-Boudreau

CEO

MIT Technology Review

Tara
Chklovski

Founder & CEO

Technovation

Sheldon
Fernandez

CEO

DarwinAI

David
Ferrucci

Founder, CEO, & Chief Scientist

Elemental Cognition

Anthony
Green

Podcast Producer

MIT Technology Review

Agrim
Gupta

PhD Student, Stanford Vision and Learning Lab

Stanford University

Mike
Haley

VP of Research

Autodesk

Will Douglas
Heaven

Senior Editor for AI

MIT Technology Review

Natasha
Jaques

Senior Research Scientist

Google Brain

Tony
Jebara

VP of Engineering and Head of Machine Learning

Spotify

Clinton
Johnson

Racial Equity Unified Team Lead

Esri

Danny
Lange

SVP of Artificial Intelligence

Unity Technologies

Julia (Xing)
Li

Deputy General Manager

Baidu USA

Darcy
MacClaren

Senior Vice President, Digital Supply Chain

SAP North America

Haniyeh
Mahmoudian

Global AI Ethicist

DataRobot

Andrew
Moore

GM and VP, Google Cloud AI

Google

Mira
Murati

SVP, Research, Product, & Partnerships

OpenAI

Prem
Natarajan

Vice President Alexa AI, Head of NLU

Amazon

Andrew
Ng

Founder and CEO

Landing AI

Amy
Nordrum

Editorial Director, Special Projects & Operations

MIT Technology Review

Kavitha
Prasad

VP & GM, Datacenter, AI and Cloud Execution and Strategy

Intel Corporation

Bali
Raghavan

Head of Engineering

Forward

Rajiv
Shah

Principal Data Scientist

Snorkel AI

Sameena
Shah

Managing Director, J.P. Morgan AI Research

JP Morgan Chase

David
Simchi-Levi

Director, Data Science Lab

MIT

Jennifer
Strong

Senior Editor for Podcasts and Live Journalism

MIT Technology Review

Fiona
Tan

CTO

Wayfair

Zenna
Tavares

Research Scientist, Columbia University; Co-Founder

Basis

Nicol
Turner Lee

Director, Center for Technology Innovation

Brookings Institution

Raquel
Urtasun

Founder & CEO

Waabi

Oriol
Vinyals

Principal Scientist

DeepMind

MIT Inside Track

David
Cox

IBM Director

MIT-IBM Watson AI Lab

Luba
Elliott

Curator, Producer, and Researcher

Creative AI

Charlotte
Jee

Reporter, News

MIT Technology Review

Naveen
Kamat

Executive Director, Data and AI Services

Kyndryl

Joseph
Lehar

Senior Vice President, R&D Strategy

Owkin

Stefanie
Mueller

Associate Professor

MIT CSAIL

Jianxiong
Xiao

Founder and CEO

AutoX

TUESDAY, MARCH 29

 

Data-Centric AI

Better Data, Better AI

Data powers AI. Good data can mean the difference between an impactful solution or one that never gets off the ground. Re-assess the foundational AI questions to ensure your data is working for, not against, you.

Innovation to Reality

The challenges of implementing AI are many. Avoid the common pitfalls with real-world case studies from leaders who have successfully turned their AI solutions into reality.

Harness What’s Possible at the Edge

With its potential for near instantaneous decision making, pioneers are moving AI to the edge. We examine the pros and cons of moving AI decisions to the edge, with the experts getting it right.

Generative AI Solutions

The use of generative AI to boost human creativity is breaking boundaries in creative areas previously untouched by AI. We explore the intersection of data and algorithms enabling collaborative AI processes to design and create.

Day 1: Data-Centric AI (9:00 a.m. – 5:20 p.m.)

Day 1: Data-Centric AI (9:00 a.m. – 5:20 p.m.)

9:00 AM

Welcome Remarks

Will Douglas Heaven

Senior Editor for AI, MIT Technology Review

Better Data, Better AI (9:10 a.m. – 10:35 a.m.)

Data powers AI. Good data can mean the difference between an impactful solution or one that never gets off the ground. Re-assess the foundational AI questions to ensure your data is working for, not against, you.

9:10 AM

Empowering Data-Centric AI

Data is the most under-valued and de-glamorized aspect of AI. Learn why shifting the focus from model/algorithm development to quality of the data is the next and most efficient, way to improve the decision-making abilities of AI.

Andrew Ng

Founder and CEO, Landing AI

9:40 AM

The Mechanics of Data-First AI

Data labeling is key to determining the success or failure of AI applications. Learn how to implement a data-first approach that can transform AI inference, resulting in better models that make better decisions.

Rajiv Shah

Principal Data Scientist, Snorkel AI

10:10 AM

Thought Leadership in Responsible AI

Question the status quo. Build stakeholder trust. These are foundational elements of thought leadership in AI. Explore how organizations can use their data and algorithms in ethical and responsible ways while building bigger and more effective systems.

Haniyeh Mahmoudian

Global AI Ethicist, DataRobot

Mainstage Break (10:35 a.m. – 11:05 a.m.)

Networking and refreshments for our live audience and a selection of curated content for those tuning in virtually.

10:35 AM

MIT Inside Track: From AI Startup to Tech “Unicorn” (available online only)

With its next-generation machine learning models fueling precision medicine, French biotech company, Owkin, captured the attention of the pharma industry. Learn how they did it and get tips to navigate the complex task of scaling your innovation.

Joseph Lehar

Senior Vice President, R&D Strategy, Owkin

Networking Break

Networking and refreshments for our live audience.

Innovation to Reality (11:05 a.m. – 12:30 p.m.)

The challenges of implementing AI are many. Avoid the common pitfalls with real-world case studies from leaders who have successfully turned their AI solutions into reality.

11:05 AM

Secrets of Successful AI Deployments

Deploying AI in real-world environments benefits from human input before and during implementation. Get an inside look at how organizations can ensure reliable results with the key questions and competing needs that should be considered when implementing AI solutions.

Andrew Moore

GM and VP, Google Cloud AI, Google

11:35 AM

From Research Lab to Real World

AI is evolving from the research lab into practical real world applications. Learn what issues should be top of mind for businesses, consumers, and researchers as we take a deep dive into AI solutions that increase modern productivity and accelerate intelligence transformation.

Julia (Xing) Li

Deputy General Manager, Baidu USA

12:00 PM

Closing the 20% Performance Gap

Getting AI to work 80% of the time is relatively straightforward, but trustworthy AI requires deployments that work 100% of the time. Unpack some of the biggest challenges that come up when eliminating the 20% gap.

Bali Raghavan

Head of Engineering, Forward

Lunch and Networking Break (12:30 p.m. – 1:30 p.m.)

12:30 PM

Lunch and Networking Break

Lunch served at the MIT Media Lab and a selection of curated content for those tuning in virtually.

Harness What’s Possible at the Edge (1:30 p.m. – 3:15 p.m.)

With its potential for near instantaneous decision making, pioneers are moving AI to the edge. We examine the pros and cons of moving AI decisions to the edge, with the experts getting it right.

1:30 PM

AI Integration Across Industries – Presented by Intel

To create sustainable business impact, AI capabilities need to be tailored and optimized to an industry or organization’s specific requirements and infrastructure model. Hear how customers’ challenges across industries can be addressed in any compute environment from the cloud to the edge with end-to-end hardware and software optimization.

Kavitha Prasad

VP & GM, Datacenter, AI and Cloud Execution and Strategy, Intel Corporation

Elizabeth Bramson-Boudreau

CEO, MIT Technology Review

1:55 PM

Explainability at the Edge

Decision making has moved from the edge to the cloud before settling into a hybrid setup for many AI systems. Through the examination of key use-cases, take a deep dive into understanding the benefits and detractors of operating a machine-learning system at the point of inference.

Sheldon Fernandez

CEO, DarwinAI

2:25 PM

AI Experiences at the Edge

Enable your organization to transform customer experiences through AI at the edge. Learn about the required technologies, including teachable and self-learning AI, that are needed for a successful shift to the edge, and hear how deploying these technologies at scale can unlock richer, more responsive experiences.

Prem Natarajan

Vice President Alexa AI, Head of NLU, Amazon

2:50 PM

The Road Ahead

Reimagine AI solutions as a unified system, instead of individual components. Through the lens of autonomous vehicles, discover the pros and cons of using an all-inclusive AI-first approach that includes AI decision-making at the edge and see how this thinking can be applied across industry.

Raquel Urtasun

Founder & CEO, Waabi

Mainstage Break (3:15 p.m. – 3:45 p.m.)

Networking and refreshments for our live audience and a selection of curated content for those tuning in virtually.

3:15 PM

Networking Break

Networking and refreshments for our live audience.

MIT Inside Track: The Impact of Creative AI (available online only)

Advances in machine learning are enabling artists and creative technologists to think about and use AI in new ways. Discuss the concept of creative AI and look at project examples from London’s art scene that illustrate the various ways creative AI is bridging the gap between the traditional art world and the latest technological innovations.

Luba Elliott

Curator, Producer, and Researcher, Creative AI

Generative AI Solutions (3:45 p.m. – 5:10 p.m.)

The use of generative AI to boost human creativity is breaking boundaries in creative areas previously untouched by AI. We explore the intersection of data and algorithms enabling collaborative AI processes to design and create.

3:45 PM

Enhancing Design through Generative AI

Change the design problem with AI. The creative nature of generative AI enhances design capabilities, finding efficiencies and opportunities that humans alone might not conceive. Explore business applications including project planning, construction, and physical design.

Mike Haley

VP of Research, Autodesk

4:15 PM

Using Synthetic Data and Simulations

Deep learning is data hungry technology. Manually labelled training data has become cost prohibitive and time-consuming. Get a glimpse at how interactive large-scale synthetic data generation can accelerate the AI revolution, unlocking the potential of data-driven artificial intelligence.

Danny Lange

SVP of Artificial Intelligence, Unity Technologies

4:40 PM

The Art of AI

Push beyond the typical uses of AI. Explore the nexus of art, technology, and human creativity through the unique innovation of kinetic data sculptures that use machines to give physical context and shape to data to rethink how we engage with the physical world.

Refik Anadol

CEO, RAS Lab; Lecturer, UCLA

Last Call with the Editors (5:10 p.m. – 5:20 p.m.)

5:10 PM

Last Call with the Editors

Before we wrap day 1, join our last call with all of our editors to get their analysis on the day’s topics, themes, and guests.

Networking Reception (5:20 p.m. – 6:20 p.m.)

WEDNESDAY, MARCH 30

Evolving the Algorithms

What’s Next for Deep Learning

Deep learning algorithms have powered most major AI advances of the last decade. We bring you into the top innovation labs to see how they are advancing their deep learning models to find out just how much more we can get out of these algorithms.

AI in Day-To-Day Business

Many organizations are already using AI internally in their day-to-day operations, in areas like cybersecurity, customer service, finance, and manufacturing. We examine the tools that organizations are using when putting AI to work.

Making AI Work for All

As AI increasingly underpins our lives, businesses, and society, we must ensure that AI must work for everyone – not just those represented in datasets, and not just 80% of the time. Examine the challenges and solutions needed to ensure AI works fairly, for all.

Envisioning the Next AI

Some business problems can’t be solved with current deep learning methods. We look at what’s around the corner at the new approaches and most revolutionary ideas propelling us toward the next stage in AI evolution.

Day 2: Evolving the Algorithms (9:00 a.m. – 5:25 p.m.)

9:00 AM

Welcome Remarks

Will Douglas Heaven

Senior Editor for AI, MIT Technology Review

What’s Next for Deep Learning (9:10 a.m. – 10:25 a.m.)

Deep learning algorithms have powered most major AI advances of the last decade. We bring you into the top innovation labs to see how they are advancing their deep learning models to find out just how much more we can get out of these algorithms.

9:10 AM

Transforming Traditional Algorithms

Transformer-based language models are revolutionizing the way neural networks process natural language. This deep dive looks at how organizations can put their data to work using transformer models. We consider the problems that business may face as these massive models mature, including training needs, managing parallel processing at scale, and countering offensive data.

Ali Alvi

Turing Group Program Manager, Microsoft

9:35 AM

Human-like Problem Solving

Critical thinking may be one step closer for AI by combining large-scale transformers with smart sampling and filtering. Get an early look at how AlphaCode’s entry into competitive programming may lead to a human-like capacity for AI to write original code that solves unforeseen problems.

Oriol Vinyals

Principal Scientist, DeepMind

10:00 AM

Aligning AI Technologies at Scale

As advanced AI systems gain greater capabilities in our search for artificial general intelligence, it’s critical to teach them how to understand human intentions. Look at the latest advancements in AI systems and how to ensure they can be truthful, helpful, and safe.

Mira Murati

SVP, Research, Product, & Partnerships, OpenAI

Mainstage Break (10:25 a.m. – 10:55 a.m.)

Networking and refreshments for our live audience and a selection of curated content for those tuning in virtually.

10:25 AM

Networking Break

Networking and refreshments for our live audience.

Business-Ready Data Holds the Key to AI Democratization – Presented by Kyndryl

Good data is the bedrock of a self-service data consumption model, which in turn unlocks insights, analytics, personalization at scale through AI. Yet many organizations face immense challenges setting up a robust data foundation. Dive into a pragmatic perspective on abstracting the complexity and untangling the conflicts in data management for better AI.

Naveen Kamat

Executive Director, Data and AI Services, Kyndryl

AI in Day-To-Day Business (10:55 a.m. – 12:20 p.m.)

Many organizations are already using AI internally in their day-to-day operations, in areas like cybersecurity, customer service, finance, and manufacturing. We examine the tools that organizations are using when putting AI to work.

10:55 AM

Improving Business Processes with AI

Effectively operationalized AI/ML can unlock untapped potential in your organization. From enhancing internal processes to managing the customer experience, get the pragmatic advice and takeaways leaders need to better understand their internal data to achieve impactful results.

Fiona Tan

CTO, Wayfair

11:25 AM

Accelerating the Supply Chain

Use AI to maximize reliability of supply chains. Learn the dos and don’ts to managing key processes within your supply chain, including workforce management, streamlining and simplification, and reaping the full value of your supply chain solutions.

Darcy MacClaren

Senior Vice President, Digital Supply Chain, SAP North America

David Simchi-Levi

Director, Data Science Lab, MIT

11:55 AM

Putting Recommendation Algorithms to Work

Machine and reinforcement learning enable Spotify to deliver the right content to the right listener at the right time, allowing for personalized listening experiences that facilitate discovery at a global scale. Through user interactions, algorithms suggest new content and creators that keep customers both happy and engaged with the platform. Dive into the details of making better user recommendations.

Tony Jebara

VP of Engineering and Head of Machine Learning, Spotify

Lunch and Networking Break (12:20 p.m. – 1:15 p.m.)

12:20 PM

Lunch and Networking Break

Lunch served at the MIT Media Lab and a selection of curated content for those tuning in virtually.

Making AI Work for All (1:15 p.m. – 2:35 p.m.)

As AI increasingly underpins our lives, businesses, and society, we must ensure that AI must work for everyone – not just those represented in datasets, and not just 80% of the time. Examine the challenges and solutions needed to ensure AI works fairly, for all.

1:15 PM

Mapping Equity

Walk through the practical steps to map and understand the nuances, outliers, and special cases in datasets. Get tips to ensure ethical and trustworthy approaches to training AI systems that grow in scope and scale within a business.

Lauren Bennett

Group Software Engineering Lead, Spatial Analysis and Data Science, Esri

Clinton Johnson

Racial Equity Unified Team Lead, Esri

1:45 PM

Bridging the AI Accessibility Gap

Get an inside look at the long- and short-term benefits of addressing inequities in AI opportunities, ranging from educating the tech youth of the future to a 10,000-foot view on what it will take to ensure that equity top is of mind within society and business alike.

Tara Chklovski

Founder & CEO, Technovation

2:10 PM

The AI Policies We Need

Public policies can help to make AI more equitable and ethical for all. Examine how policies could impact corporations and what it means for building internal policies, regardless of what government adopts. Identify actionable ideas to best move policies forward for the widest benefit to all.

Nicol Turner Lee

Director, Center for Technology Innovation, Brookings Institution

Mainstage Break (2:35 p.m. – 3:05 p.m.)

Networking and refreshments for our live audience and a selection of curated content for those tuning in virtually.

2:35 PM

Networking Break

Networking and refreshments for our live audience.

MIT Inside Track: Accelerating the Advent of Autonomous Driving (available online only)

From the U.S. to China, the global robo-taxi race is gaining traction with consumers and regulators alike. Go behind the scenes with AutoX – a Level 4 driving technology company – and hear how it overcame obstacles while launching the world’s second and China’s first public, fully driverless robo-taxi service.

Jianxiong Xiao

Founder and CEO, AutoX

Envisioning the Next AI (3:05 p.m. – 4:50 p.m.)

Some business problems can’t be solved with current deep learning methods. We look at what’s around the corner at the new approaches and most revolutionary ideas propelling us toward the next stage in AI evolution.

3:05 PM

How AI Is Powering the Future of Financial Services – Presented by JP Morgan Chase

The use of AI in finance is gaining traction as organizations realize the advantages of using algorithms to streamline and improve the accuracy of financial tasks. Step through use cases that examine how AI can be used to minimize financial risk, maximize financial returns, optimize venture capital funding by connecting entrepreneurs to the right investors; and more.

Sameena Shah

Managing Director, J.P. Morgan AI Research, JP Morgan Chase

3:30 PM

Evolution of Mind and Body

In a study of simulated robotic evolution, it was observed that more complex environments and evolutionary changes to the robot’s physical form accelerated the growth of robot intelligence. Examine this cutting-edge research and decipher what this early discovery means for the next generation of AI and robotics.

Agrim Gupta

PhD Student, Stanford Vision and Learning Lab, Stanford University

4:00 PM

A Path to Human-like Common Sense

Understanding human thinking and reasoning processes could lead to more general, flexible and human-like artificial intelligence. Take a close look at the research building AI inspired by human common-sense that could create a new generation of tools for complex decision-making.

Zenna Tavares

Research Scientist, Columbia University; Co-Founder, Basis

4:25 PM

Social Learning Bots

Look under the hood at this innovative approach to AI learning with multi-agent and human-AI interactions. Discover how bots work together and learn together through personal interactions. Recognize the future implications for AI, plus the benefits and obstacles that may come from this new process.

Natasha Jaques

Senior Research Scientist, Google Brain

Closing Segment (4:50 p.m. – 5:25 p.m.)

4:50 PM

Pulling Back the Curtain on AI

David Ferrucci was the principal investigator for the team that led IBM Watson to its landmark Jeopardy success, awakening the world to the possibilities of AI. We pull back the curtain on AI for a wide-ranging discussion on explicable models, and the next generation of human and machine collaboration creating AI thought partners with limitless applications.

David Ferrucci

Founder, CEO, & Chief Scientist, Elemental Cognition

5:15 PM

Closing Remarks

Closing Toast (5:25 p.m. – 5:45 p.m.)

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@MIT Artificial intelligence system rapidly predicts how two proteins will attach: The model called Equidock, focuses on rigid body docking — which occurs when two proteins attach by rotating or translating in 3D space, but their shapes don’t squeeze or bend

Reporter: Aviva Lev-Ari, PhD, RN

This paper introduces a novel SE(3) equivariant graph matching network, along with a keypoint discovery and alignment approach, for the problem of protein-protein docking, with a novel loss based on optimal transport. The overall consensus is that this is an impactful solution to an important problem, whereby competitive results are achieved without the need for templates, refinement, and are achieved with substantially faster run times.
28 Sept 2021 (modified: 18 Nov 2021)ICLR 2022 SpotlightReaders:  Everyone Show BibtexShow Revisions
 
Keywords:protein complexes, protein structure, rigid body docking, SE(3) equivariance, graph neural networks
AbstractProtein complex formation is a central problem in biology, being involved in most of the cell’s processes, and essential for applications such as drug design or protein engineering. We tackle rigid body protein-protein docking, i.e., computationally predicting the 3D structure of a protein-protein complex from the individual unbound structures, assuming no three-dimensional flexibility during binding. We design a novel pairwise-independent SE(3)-equivariant graph matching network to predict the rotation and translation to place one of the proteins at the right location and the right orientation relative to the second protein. We mathematically guarantee that the predicted complex is always identical regardless of the initial placements of the two structures, avoiding expensive data augmentation. Our model approximates the binding pocket and predicts the docking pose using keypoint matching and alignment through optimal transport and a differentiable Kabsch algorithm. Empirically, we achieve significant running time improvements over existing protein docking software and predict qualitatively plausible protein complex structures despite not using heavy sampling, structure refinement, or templates.
One-sentence SummaryWe perform rigid protein docking using a novel independent SE(3)-equivariant message passing mechanism that guarantees the same resulting protein complex independent of the initial placement of the two 3D structures.
 
SOURCE
 

MIT researchers created a machine-learning model that can directly predict the complex that will form when two proteins bind together. Their technique is between 80 and 500 times faster than state-of-the-art software methods, and often predicts protein structures that are closer to actual structures that have been observed experimentally.

This technique could help scientists better understand some biological processes that involve protein interactions, like DNA replication and repair; it could also speed up the process of developing new medicines.

Deep learning is very good at capturing interactions between different proteins that are otherwise difficult for chemists or biologists to write experimentally. Some of these interactions are very complicated, and people haven’t found good ways to express them. This deep-learning model can learn these types of interactions from data,” says Octavian-Eugen Ganea, a postdoc in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-lead author of the paper.

Ganea’s co-lead author is Xinyuan Huang, a graduate student at ETH Zurich. MIT co-authors include Regina Barzilay, the School of Engineering Distinguished Professor for AI and Health in CSAIL, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering in CSAIL and a member of the Institute for Data, Systems, and Society. The research will be presented at the International Conference on Learning Representations.

Significance of the Scientific Development by the @MIT Team

EquiDock wide applicability:

  • Our method can be integrated end-to-end to boost the quality of other models (see above discussion on runtime importance). Examples are predicting functions of protein complexes [3] or their binding affinity [5], de novo generation of proteins binding to specific targets (e.g., antibodies [6]), modeling back-bone and side-chain flexibility [4], or devising methods for non-binary multimers. See the updated discussion in the “Conclusion” section of our paper.

 

Advantages over previous methods:

  • Our method does not rely on templates or heavy candidate sampling [7], aiming at the ambitious goal of predicting the complex pose directly. This should be interpreted in terms of generalization (to unseen structures) and scalability capabilities of docking models, as well as their applicability to various other tasks (discussed above).

 

  • Our method obtains a competitive quality without explicitly using previous geometric (e.g., 3D Zernike descriptors [8]) or chemical (e.g., hydrophilic information) features [3]. Future EquiDock extensions would find creative ways to leverage these different signals and, thus, obtain more improvements.

   

Novelty of theory:

  • Our work is the first to formalize the notion of pairwise independent SE(3)-equivariance. Previous work (e.g., [9,10]) has incorporated only single object Euclidean-equivariances into deep learning models. For tasks such as docking and binding of biological objects, it is crucial that models understand the concept of multi-independent Euclidean equivariances.

  • All propositions in Section 3 are our novel theoretical contributions.

  • We have rewritten the Contribution and Related Work sections to clarify this aspect.

   


Footnote [a]: We have fixed an important bug in the cross-attention code. We have done a more extensive hyperparameter search and understood that layer normalization is crucial in layers used in Eqs. 5 and 9, but not on the h embeddings as it was originally shown in Eq. 10. We have seen benefits from training our models with a longer patience in the early stopping criteria (30 epochs for DIPS and 150 epochs for DB5). Increasing the learning rate to 2e-4 is important to speed-up training. Using an intersection loss weight of 10 leads to improved results compared to the default of 1.

 

Bibliography:

[1] Protein-ligand blind docking using QuickVina-W with inter-process spatio-temporal integration, Hassan et al., 2017

[2] GNINA 1.0: molecular docking with deep learning, McNutt et al., 2021

[3] Protein-protein and domain-domain interactions, Kangueane and Nilofer, 2018

[4] Side-chain Packing Using SE(3)-Transformer, Jindal et al., 2022

[5] Contacts-based prediction of binding affinity in protein–protein complexes, Vangone et al., 2015

[6] Iterative refinement graph neural network for antibody sequence-structure co-design, Jin et al., 2021

[7] Hierarchical, rotation-equivariant neural networks to select structural models of protein complexes, Eismann et al, 2020

[8] Protein-protein docking using region-based 3D Zernike descriptors, Venkatraman et al., 2009

[9] SE(3)-transformers: 3D roto-translation equivariant attention networks, Fuchs et al, 2020

[10] E(n) equivariant graph neural networks, Satorras et al., 2021

[11] Fast end-to-end learning on protein surfaces, Sverrisson et al., 2020

SOURCE

https://openreview.net/forum?id=GQjaI9mLet

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