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Companies Actively Engaging Artificial Intellegence for Drug Design: 2026

Posted in Artificial Intelligence Applications in Health Care, Artificial Intelligence in CANCER, Artificial Intelligence in Medicine - Applications in Therapeutics, Deep Learning, Drug Development Process, Machine Learning, Pharmaceutical Drug Discovery, Pharmaceutical Industry Competitive Intelligence, tagged , , , , , , on July 31, 2026| Leave a Comment »

Companies Actively Engaging Artificial Intellegence for Drug Design: 2026

Curator: Stephen J. Williams, Ph.D.

Work-in-Progress

This is not an exhaustive list of all AI companies involved in the drug discovery process.  Some like Chai Discovery will be featured in separate posts and this will be constantly updated as companies are acquired and formed. 

UPDATED 8/31/2026

First Bankruptcy Casualty for AI-Drug Discovery Firm: BioXcel Files for Chapter 11 Bankruptcy and sells Alzheimers Pipeline to Teva Pharmaceuticals

Aug 27 (Reuters) – AI-driven biopharma firm BioXcel Therapeutics filed for Chapter 11 bankruptcy protection in the U.S. on Thursday.

Here are more details:

• BioXcel listed estimated liabilities in the range of $100 million to $500 million and assets of $10 million to $50 million in its petition filed with U.S. Bankruptcy Court for the District of Delaware.

• The filing comes after BioXcel entered into an amended credit agreement on Monday, under which creditors would provide an additional $1.25 million in loans to the company.

• BioXcel Therapeutics reported a quarterly adjusted loss of 49 cents​​ per share for the three months ended June 30.

• The firm, whose shares have fallen about 55% so far this year, said in May it was exploring strategic options including a sale, merger or licensing agreement of its assets, but did not provide a timeline.

• BioXcel is focused on developing neurological medication. Its experimental drug BXCL501 is marketed as an acute treatment for agitation associated with Alzheimer’s dementia.

(Reporting by Anusha Shah in Bengaluru; Editing by Eileen Soreng)

Source: https://srnnews.com/biopharma-firm-bioxcel-therapeutics-files-for-bankruptcy-in-us/ 

About BioXcel: Source https://www.bioxceltherapeutics.com/ai-based-drug-re-innovation/

The traditional paradigm in the pharmaceutical industry of selecting new chemical entities that are put through lead identification and selection in drug discovery and development is marred by decade-long timelines, high costs that increase every year, and low success rates. Consequently, many diseases and medical conditions remain without treatments. Yet, an underutilized and expansive universe of potential drug candidates exists that consists of approved products that could be effective for additional indications as well as compounds that have already demonstrated safety in prior clinical trials but may have been discontinued by their original developers for various reasons.

BioXcel Therapeutics is focused on AI-based drug re-innovation in neuroscience. We expedite the discovery and development of new indications for existing late-stage drug candidates and/or approved drugs by leveraging NovareAI: a composite of AI tools and approaches. NovareAI enables us to reduce therapeutic development costs, potentially accelerate timelines, while improving the success rate of bringing new treatment options to patients.

Original Article Starts Here

I want to start with a Great interview and white paper by McKinsey where they interview key opinion leaders on the business intelligence on AI firms in the drug discovery world and what to look for in companies that are in this space.

Source: https://www.mckinsey.com/industries/life-sciences/our-insights/how-ai-could-revolutionize-drug-discovery

How AI could revolutionize drug discovery
 | Video
By Alex Devereson, Christoph Sandler, and Lydia The
Watch VIDEO
Artificial intelligence could help scientists develop better medicines faster—and thus improve millions of people’s lives. But for that to happen, companies will need to change the way they work.

Below is a Mckinsey report on How AI will Change the Future of Biotech

https://www.mckinsey.com/featured-insights/the-next-normal/biotech

CHARTING THE FUTURE

AI will be embedded into everyday research

The AI-driven drug discovery industry continues to grow, fueled by new entrants in the market, significant capital investment, and technology maturation. We’ve identified more than 250 companies working in the industry. More than half of them are based in the United States, but key hubs are emerging in Western Europe and Southeast Asia as well. The best of these companies will fully integrate AI into research workflows, as the exhibit shows. By putting AI at the center of the research engine, companies can transform research at scale—and bring about dramatic improvements in patient outcomes. For the full article, see:

Parts of a high-throughput screening (HTS) process embedded with AI technology

1. High-throughput screen commenced with diverse compound sets Scientist selects diverse compound sets (a set of chemical compounds with a wide range of chemical structures) as first high-throughput screen

  • In silico/ on-the-chip simulations
  • In-vitro/ ‘wet lab’ experiments

2. Automated compound selection and transfer Using HTS machinery, individual compounds are transferred to individual wells of cells under experimental conditions

3. Computer-vision-based hit selection Cell response to each compound is measured using microscope analysis (eg, through computer vision techniques); promising compounds are labeled “hits”

4. Automated machine learning (ML) model training from screen outcomes Information from HTS for first few plates is automatically transferred into an ML pipeline, which “learns” how cells respond to each kind of chemical structure

5. Compound library inferencing and prioritization ML algorithm then scans the remainder of the library compounds and predicts which plates should be prioritized to identify the highest number of hits in the next screen

6. Automated compound selection based on ML recommendations ML recommendations are automatically queued and used in the next round of HTS. The cycle continues, with the algorithm continuously learningfrom “real world” outputs. Recommendations trigger scientists to explore new chemical space and begin downstream screening processes more quickly. These recommendations feed into the selection of chemical compounds in step 1

DOWNLOADS

Human bodies are incredibly complex. It takes many years to discover even just one new medicine to successfully treat a disease. Could artificial intelligence help speed up that process? McKinsey experts believe so. (The following transcript has been edited for clarity.)

Video

How AI could revolutionize drug discovery

Faster and better

 

How relevant and useful is this article for you?

Lydia The: What excites me about AI and drug discovery is the convergence between technology, drug development, and biology, which is going to lead to better drugs being developed faster—using all of the capabilities that Silicon Valley and the tech ecosystem have developed—to help us have even greater impact on patients.

Christoph Sandler: Today, to discover and develop a drug takes more than ten years.

Alex Devereson: We might be able to have drugs in one-tenth of the time, from being discovered to being able to treat patients. Today, many diseases simply have no treatments whatsoever. I think, and I hope, we’re going to see a world where we can generate therapies that can treat those patients very effectively. Fundamentally, we will have life-changing, game-changing drugs—on a scale and at a pace that we’ve never seen before—getting to the right patient at the right time.

The promise of personalized medicine

Christoph Sandler: In the not-so-distant future, we might collect health data across different inputs: from wearables, from our electronic medical records, or from clinical or academic research. And we will have the opportunity, on a voluntary basis, to upload these data into a central, secure, trusted data storage system.

Lydia The: You could imagine using the data to figure out not just what drug might work for you but exactly what drug would work for you at what time, in what sequence, in what dose—really personalized to you.

Will AI replace scientists?

Lydia The: What we’ve found is that technology doesn’t supplant the people. Rather, it will enable scientists to do things faster and better—and potentially develop insights that humans would not be able to develop at all.

Alex Devereson: Scientists will be able to discover things with machine learning that they could never have thought of by themselves, generate entirely new ideas, and move at a pace at which one person can do what it would have taken 100 to do before.

Lydia The: While in previous generations, scientists would spend a lot of their time—maybe even the majority of their time—on manual efforts such as pipetting from one tray to another tray or manually curating and cleaning data, I think AI will help us do all of those things in a more automated and quick way, and develop hypotheses that can then lead a scientist to think through, “What would the next experiment be? What are the implications of the data?”

What companies should do today

Alex Devereson: I think the challenge a lot of companies have is that they want to explore new ideas but, for good reason, they are reluctant to commit until they’ve seen some results and some tangible impact. So they explore a lot of pilots.

Lydia The: We have a term for that. We call it “pilot purgatory”: companies focus on one pilot, and they see the returns in a single pilot, but they don’t establish that approach and way of operating across their organization.

Define a ‘North Star’

Lydia The: One of the most important things for companies to escape pilot purgatory is a real mindset shift, from the top end all the way through the rest of the organization.

Christoph Sandler: It is important for the organization to define what a North Star for them should be, so that the data and analytics transformation of the R&D function can be targeted toward that North Star.

Identify—and solve—the biggest pain points

Alex Devereson: Truly understand what your biggest scientific and operational pain points are. For the lab scientists, for the patients who are in your clinical trials, for the patients who might get the drug: What is the biggest unsolved problem today?

Embed analytics into decision making

Christoph Sandler: Bring the organization on board and help them understand the potential of data and analytics.

Alex Devereson: You need to really, truly redesign a process that embeds analytics, where it’s not something on the side; it’s truly a part of the decision making.

Christoph Sandler: And you need to establish trust in the data and in these models. Equally important as the technical part is the human part.

Deliver value quickly

Alex Devereson: Don’t set up a project that delivers only after five years. Have a view on how you can deliver value in three months—and on what it takes in terms of analytics, data, and technology—with a relentless laser focus on value for the patient and for the scientific process.

 

 

Source: https://www.statnews.com/2026/06/03/alnylam-partner-with-inceptive-nucleics-ai-foundation-models/?utm_campaign=the_readout&utm_medium=email&_hsenc=p2ANqtz-_DOWjLTkMjWQ-ewilam07aNlz79w35ktjIKopXav_x3PpkOIfQIqnnMJlxdznIvjv-lGeo3tI4BUwIuxqEclEu4fHA9Q&_hsmi=422199873&utm_content=422199873&utm_source=hs_email
Biotech Correspondent

The inventor of some of AI’s technical underpinnings is shifting focus to an RNA startup. Also, Rick Pazdur has some ideas on how to determine whether Revolution Medicines’ pancreatic cancer drug can work in a first-line setting. And an oncologist explains why she believes the failed Grail trial matters.

artificial intelligence

The AI architect taking aim at RNAi

Jakob Uszkoreit helped create the transformer architecture — the “T” in ChatGPT — that sparked the generative AI boom. Now he’s trying to do something just as ambitious in drug development, STAT’s Brittany Trang writes. His startup, Inceptive Nucleics, is building biological foundation models that can be applied across a wide range of sequence-based medicines, from RNA interference therapies to mRNA and antisense drugs.

That vision caught the attention of Alnylam, which yesterday announced that it had struck a three-year partnership worth up to $2 billion in potential milestone payments and royalties, along with a $30 million upfront investment. The idea is that AI could perhaps do more than analyze biological data — and instead design the molecules themselves.

 

Top 10 Leading AI Drug Discovery Companies Transforming the Market: Trends, Technologies, Growth Outlook & Competitive Landscape (2026-2034)

IntelMarketResearch

MARKET OVERVIEW:

Artificial Intelligence (AI) is redefining the future of pharmaceutical research, making drug development faster, smarter, and more cost-effective than ever before. What once took more than a decade and billions of dollars can now be significantly accelerated through advanced machine learning algorithms, generative AI, predictive analytics, and computational biology.

AI Drug Discovery Market is projected to grow from USD 3.41 billion in 2026 to USD 10.45 billion by 2034, registering a robust 15.7% CAGR. This remarkable expansion reflects the pharmaceutical industry’s increasing reliance on AI-powered platforms to improve research efficiency, reduce clinical failures, and identify breakthrough therapies for complex diseases.

As healthcare systems worldwide demand faster innovation and precision medicine continues to evolve, AI is becoming an essential component of modern drug discovery rather than an experimental technology.

Key Technologies Powering AI Drug Discovery

The industry’s rapid growth is supported by several advanced technologies that work together throughout the drug development pipeline.

Machine Learning helps identify disease targets, recognize biological patterns, and predict therapeutic outcomes using historical datasets.

Deep Learning improves molecular analysis by recognizing highly complex biological relationships that conventional computational methods may overlook.

Generative AI creates entirely new molecular structures designed for specific therapeutic applications, enabling scientists to explore millions of virtual compounds before physical synthesis.

Predictive Modeling estimates toxicity, efficacy, pharmacokinetics, and safety profiles early in development, reducing unnecessary experimentation.

Molecular Docking Simulations evaluate how drug molecules interact with proteins, helping researchers prioritize candidates with the highest therapeutic potential.

AI-Driven Clinical Trial Optimization assists pharmaceutical companies in selecting suitable patient populations, improving trial design, and increasing the probability of successful outcomes.

Top 10 Leading AI Drug Discovery Companies Driving Innovation

The competitive landscape is rapidly evolving, with established technology providers and specialized biotechnology companies competing to develop next-generation AI platforms.

1. Insilico Medicine

Insilico Medicine has become one of the industry’s most recognized innovators through its end-to-end AI drug discovery platform. The company combines generative AI with biological research to accelerate target identification and therapeutic development.

2. Exscientia

Exscientia focuses on AI-designed precision medicines and has established multiple collaborations with leading pharmaceutical organizations to improve drug candidate selection and optimization. Exscientia is a pioneering artificial intelligence-driven pharmatech company that was acquired by Recursion Pharmaceuticals in November 2024

3. Atomwise

Known for its deep learning platform, Atomwise uses artificial intelligence to identify promising small-molecule therapies across oncology, infectious diseases, and rare disorders.

4. Recursion Pharmaceuticals

Recursion Pharmaceuticals integrates high-content imaging, automation, and AI to analyze millions of biological experiments, enabling large-scale phenotypic drug discovery.

5. BERG

BERG (now operating or known as BPGbio) specializes in biology-driven AI by combining multi-omics datasets with artificial intelligence to identify biomarkers and novel therapeutic opportunities.

6. Cyclica

Cyclica’s AI platform focuses on polypharmacology, helping researchers understand complex drug-protein interactions while improving drug design efficiency. , focusing on MatchMaker and POEM technology platforms before being acquired by Recursion for $40 million in May 2023.

7. GNS Healthcare

GNS Healthcare applies causal AI and patient-level analytics to support personalized medicine and identify drug repurposing opportunities.

8. DeepCure

DeepCure develops generative chemistry platforms capable of designing optimized molecules while reducing early-stage research complexity.

9. Schrödinger

Schrödinger combines computational chemistry, molecular simulation, and AI technologies to improve molecular modeling and accelerate pharmaceutical innovation.

10. IBM Watson Health & DeepMind Health

These technology pioneers continue expanding AI capabilities across healthcare by leveraging advanced analytics, large-scale biological data processing, and intelligent research platforms that support drug development initiatives. https://www.ibm.com/products/watsonx

There are many other companies that are using AI in Innovative ways to support drug discovery.  Here is another list from

 

11 Innovative Companies Using AI for Drug Discovery

Contributing Writer

By Sherri Gordon

Published December 15, 2025

Overview

A look at 11 companies using AI-driven platforms to reshape drug discovery, including generative models, computational chemistry, and data-centric biology.

 

For more articles on AI and healthcare on this Open Access Scientific Journals please see our Portals at

Medicine with GPT-4 & Chat GPT

AGI, generativeAI, Grok, DeepSeek & Expert Models in Healthcare

Artificial Intelligence: Genomics & Cancer

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Geneticist George Church: A Future Without Limits

Posted in BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, BioTechnology - Venture Creation, Genomic Testing: Methodology for Diagnosis, Interviews with Key Opinion Leaders (KOLs), Investment in Technological Breakthrough, Massachusetts Academy of Sciences (MAS)  , Molecular Genetics & Pharmaceutical, Personalized and Precision Medicine & Genomic Research, Personalized Medicine Coalition, Precision Cancer Medicine, RNA Biology, Cancer and Therapeutics, Scoop.it, Transcriptomics, Translational Science, tagged , , , , , , , , , on October 24, 2014| Leave a Comment »

Geneticist George Church: A Future Without Limits

Reporter: Aviva Lev-Ari, PhD, RN

Article ID #155: Geneticist George Church: A Future Without Limits. Published on 10/24/2014

WordCloud Image Produced by Adam Tubman

UPDATED 12/05/2020

 

In the future, George Church believes, almost everything will be better because of genetics. If you have a medical problem, your doctor will be able to customize a treatment based on your specific DNA pattern. When you fill up your car, you won’t be draining the world’s dwindling supply of crude oil, because the fuel will come from microbes that have been genetically altered to produce biofuel. When you visit the zoo, you’ll be able to take your children to the woolly mammoth or passenger pigeon exhibits, because these animals will no longer be extinct. You’ll be able to do these things, that is, if the future turns out the way Church envisions it—and he’s doing everything he can to see that it does.

UPDATED 12/05/2020

George Church backs a startup solution to the massive gene therapy manufacturing bottleneck

Source: https://endpts.com/george-church-backs-a-startup-solution-to-the-massive-gene-therapy-manufacturing-bottleneck/
Jason Mast: Associate Editor
George Church and his graduate students have spent the last decade seeding startups on the razor’s edge between biology and science fiction: gene therapy to prevent aging, CRISPRed pigs that can be used to harvest organs for transplant, and home kits to test your poop for healthy or unhealthy bacteria. (OK, maybe they’re not all on that razor’s edge.)

But now a new spinout from the Department of Genetics’ second floor is tackling a far humbler problem — one that major company after major company has stumbled over as they tried to get cures for rare diseases and other gene therapies into the clinic and past regulators: How the hell do you build these?

CEO Lex Vovner of 64x Bio

“There’s a lot happening for new therapies but not enough attention around this problem,” Lex Rovner, who was a post-doc at Church’s lab from 2015 to 2018, told Endpoints News. “And if we don’t figure out how to fix this, many of these therapies won’t even reach patients.”

This week, with Church and a couple other prominent scientists as co-founders, Rovner launched 64x Bio to tackle one key part of the manufacturing bottleneck. They won’t be looking to retrofit plants or build gene therapy factories, as Big Pharma and big biotech are now spending billions to do. Instead, with $4.5 million in seed cash, they will try to engineer the individual cells that churn out a critical component of the therapies.

George Church
The goal is to build cells that are fine-tuned to do nothing but spit out the viral vectors that researchers and drug developers use to shuttle gene therapies into the body. Different vectors have different demands; 64x Bio will look to make efficient cellular factories for each.

“While a few general ways to increase vector production may exist, each unique vector serotype and payload poses a specific challenge,” Church said in an emailed statement. “Our platform enables us to fine tune custom solutions for these distinct combinations that are particularly hard to overcome.”

Before joining Church’s lab, Rovner did her graduate work at Yale, where she studied how to engineer bacteria to produce new kinds of protein for drugs or other purposes. And after leaving Church’s lab in 2018, she initially set out to build a manufacturing startup with a broad focus.

Yet as she spoke with hundreds of biotech executives on LinkedIn and in coffee shops around Cambridge, the same issue kept popping up: They liked their gene therapy technology in the lab but they didn’t know how to scale it up.

“Everyone kept saying the same thing,” Rovner said. “We basically realized there’s this huge problem.”

The issue would soon make headlines in industry publications: bluebird delaying the launch of Zynteglo, Novartis delaying the launch of Zolgensma in the EU, Axovant delaying the start of their Parkinson’s trial.

Part of the problem, Rovner said, is that gene therapies are delivered on viral vectors. You can build these vectors in mammalian cell lines by feeding them a small circular strand of DNA called a plasmid. The problem is that mammalian cells have, over billions of years, evolved tools and defenses precisely to avoid making viruses. (Lest the mammal they live in die of infection).

There are genetic mutations that can turn off some of the internal defenses and unleash a cell’s ability to produce virus, but they’re rare and hard to find. Other platforms, Rovner said, try to find these mutations by using CRISPR to knock out genes in different cells and then screening each of them individually, a process that can require hundreds of thousands of different 100-well plates, with each well containing a different group of mutant cells.

“It’s just not practical, and so these platforms never find the cells,” Rovner said.

64x Bio will try to find them by building a library of millions of mutant mammalian cells and then using a molecular “barcoding” technique to screen those cells in a single pool. The technique, Rovner said, lets them trace how much vector any given cell produces, allowing researchers to quickly identify super-producing cells and their mutations.

The technology was developed partially in-house but draws from IP at Harvard and the Wyss Institute. Harvard’s Pam Silver and Wyss’s Jeffrey Way are co-founders.

The company is now based in SoMa in San Francisco. With the seed cash from Fifty Years, Refactor and First Round Capital, Rovner is recruiting and looking to raise a Series A soon. They’re in talks with pharma and biotech partners, while they try to validate the first preclinical and clinical applications.

Gene therapy is one focus, but Rovner said the platform works for anything that involves viral vector, including vaccines and oncolytic viruses. You just have to find the right mutation.

“It’s the rare cell you’re looking for,” she said.

AUTHOR
Jason Mast
Associate Editor
jason@endpointsnews.com
@JasonMMast
Jason Mas

In 2005 he launched the Personal Genome Project, with the goal of sequencing and sharing the DNA of 100,000 volunteers. With an open-source database of that size, he believes, researchers everywhere will be able to meaningfully pursue the critical task of correlating genetic patterns with physical traits, illnesses, and exposure to environmental factors to find new cures for diseases and to gain basic insights into what makes each of us the way we are. Church, tagged as subject hu43860C, was first in line for testing. Since then, more than 13,000 people in the U.S., Canada, and the U.K. have volunteered to join him, helping to establish what he playfully calls the Facebook of DNA.

Church has made a career of defying the impossible. Propelled by the dizzying speed of technological advancement since then, the Personal Genome Project is just one of Church’s many attempts to overcome obstacles standing between him and the future.

“It’s not for everyone,” he says. “But I see a trend here. Openness has changed since many of us were young. People didn’t use to talk about sexuality or cancer in polite society. This is the Facebook generation.” If individuals were told which diseases or medical conditions they were genetically predisposed to, they could adjust their behavior accordingly, he reasoned. Although universal testing still isn’t practical today, the cost of sequencing an individual genome has dropped dramatically in recent years, from about $7 million in 2007 to as little as $1,000 today.

“It’s all too easy to dismiss the future,” he says. “People confuse what’s impossible today with what’s impossible tomorrow.”, especially through the emerging discipline of “synthetic” biology. The basic idea behind synthetic biology, he explained, was that natural organisms could be reprogrammed to do things they wouldn’t normally do, things that might be useful to people. In pursuit of this, researchers had learned not only how to read the genetic code of organisms but also how to write new code and insert it into organisms. Besides making plastic, microbes altered in this way had produced carpet fibers, treated wastewater, generated electricity, manufactured jet fuel, created hemoglobin, and fabricated new drugs. But this was only the tip of the iceberg, Church wrote. The same technique could also be used on people.

“Every cell in our body, whether it’s a bacterial cell or a human cell, has a genome,” he says. “You can extract that genome—it’s kind of like a linear tape—and you can read it by a variety of methods. Similarly, like a string of letters that you can read, you can also change it. You can write, you can edit it, and then you can put it back in the cell.”

This April, the Broad Institute, where Church holds a faculty appointment, was awarded a patent for a new method of genome editing called CRISPR (clustered regularly interspersed short palindromic repeats), which Church says is one of the most effective tools ever developed for synthetic biology. By studying the way that certain bacteria defend themselves against viruses, researchers figured out how to precisely cut DNA at any location on the genome and insert new material there to alter its function. Last month, researchers at MIT announced they had used CRISPR to cure mice of a rare liver disease that also afflicts humans. At the same time, researchers at Virginia Tech said they were experimenting on plants with CRISPR to control salt tolerance, improve crop yield, and create resistance to pathogens.

The possibilities for CRISPR technology seem almost limitless, Church says. If researchers have stored a genetic sequence in a computer, they can order a robot to produce a piece of DNA from the data. That piece can then be put into a cell to change the genome. Church believes that CRISPR is so promising that last year he co-founded a genome-editing company, Editas, to develop drugs for currently incurable diseases.

Source: news.nationalgeographic.com

See on Scoop.it – Cardiovascular and vascular imaging

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