It’s Been a Decade of AI in the Drug Discovery Race. What’s Next?

by Andrii Buvailo   | 

The year 2012 is often regarded as the beginning of the deep learning revolution. 

That year, Alexnet, the deep learning model proposed in the research paper Imagenet Classification with Deep Convolutional Neural Network by Alex Krizhevsky and his colleagues, won the large-scale visual recognition challenge ImageNet -- completely dominating over other machine learning competitors. 

Another famous event of that year was spontaneous identification of a cat by another deep neural network -- in millions of random YouTube videos. 

Scientists at Google's mysterious X lab built a neural network of 16,000 computer processors with one billion connections, and it was never told to look for cats, nor was it told what a cat was. Artificial intelligence learned that by itself and flagged the pattern of pixels corresponding to a cat’s face. 

While the concept of deep learning was known since the 1960s, the technology only started manifesting itself as a practically viable thing in the 2010s, mainly due to sufficient progress in computational power allowing it to train such complex models. 

Additionally, 2000-2010s were marked by the increasing availability of big datasets for training purposes, such as ImageNet, and the rise of computational infrastructure, like Elastic Compute Cloud by Amazon -- other important drivers of the deep learning revolution which started unfolding in 2010-2012. 


Historical trajectory of AI in drug discovery

It is no wonder then, that more than 80% of more than 500 AI-augmented drug discovery and development startups and scaleups recorded in BiopharmaTrend database “The State of AI in the Biopharma Industry” were founded around 2012 and later, around the time deep learning became a major trend. 



Some of the early AI companies, founded in 2012, included Atomwise (structure based small molecule screening), Exscientia (target discovery and small molecule design), AbCellera (genomics-driven antibody design), and Flatiron Health (a clinical data integrator and analytical powerhouse for clinical research in oncology). 



A year later, other notable AI-driven players emerged, including BenevolentAI, Cyclica, and Recursion Pharmaceuticals -- in the small molecule drug discovery space, and Zymergen in biotech.



But the progress in the artificial intelligence space accelerated, and the year 2014 saw another major milestone, later called by Yann LeCun, Facebook's chief AI scientist at that time, “the coolest idea in deep learning in the last 20 years.”  

In June 2014, Ian Goodfellow published a seminal paper introducing Generative Adversarial Networks (GANs). Not only did this breakthrough concept transform the area of generative AI, but it also won his inventor a place in history as The GANfather: The man who’s given machines the gift of imagination.

This breakthrough stimulated the next spin of innovation and drug discovery entrepreneurship, including probably the most notable AI-driven company founded in the year 2014 -- Insilico Medicine

Co-founded by Dr. Alex Zhavoronkov, Insilico Medicine pioneered the application of deep learning for small molecule drug design and later -- target discovery. The company also incorporated GAN technology, and later built a number of GAN-based computational tools for drug discovery, including DruGAN for fingerprints, ORGAN for SMILEs, etc.

As per information I have received by email from Insilico Medicine, most of their targets are novel or have sufficient novelty aspect, and they managed to discover both novel targets and molecules for those targets relying mostly on their Pharma.AI engine (see Table 1 below for detailed pipeline information).



This year, a prominent AI company XtalPi was founded on the MIT campus and in China, focusing from its inception on combining quantum physics, AI, cloud computing, and large-scale clusters of robotic workstations.

Interestingly, the company initially disclosed an internal pipeline, including at least 10 early stage discovery programs for a number of indications. However, at present, the company is focusing exclusively on partnerships and contract research services.

As the a-1 filing documents indicate:

“We have entered into a number of collaborations with biotechnology and pharmaceutical companies and academic institutions under which our collaborators pursue research in a number of therapeutic areas, such as oncology, neurology, respirology, and inflammatory diseases. In some cases, we retain at least partial ownership in the pipeline programs, typically in the double-digit percentage range, of the programs pursued under these collaborations. We are not responsible for advancing their pre-clinical development beyond generation of pre-clinical candidates.”

As per company email for BiopharmaTrend, XtalPi never intended to pursue these projects in the same sense the other companies build up their pipelines. When they first launched their one-stop drug discovery business, the company started several proof-of-concept discovery projects in-house to showcase capabilities to clients and accumulate R&D data.

Currently, XtalPi offers drug discovery solutions (both in small molecules and antibodies) that are powered by AI and (robotics) wet lab, which play a more prominent role in the zero-to-one innovation process of identifying novel molecules for clinical candidates.



Many notable AI-focused companies were founded in the coming years to tackle various aspects of drug discovery and development, including Insitro, Relay Therapeutics, Valo Health, Verge Genomics and others.  

We should mention one notable exception -- New York-based AI drug discovery company Schrödinger, which was founded in 1990, well before the deep learning era. 

In the early years, the company was known as a well-established developer of cheminformatics solutions and software for drug research. Over the last decade, Schrödinger picked the AI trend as well and substantially augmented its product offering, launched novel AI-based tools, and created an internal drug candidate pipeline eventually, with two Phase 1 assets (Table 1). 

Trying to comprehend the market of AI in drug discovery and development,  it should be noted that a broad category of the 500+ AI startups recorded in BiopharmaTrend report are not developing own drug candidates, they have no internal pipelines. 

For instance, many successful companies on the list, like CytoReason (AI-enabled disease modeling) or BenchSci (AI-driven search engine for scientific reagents), contribute meaningfully to the advent of artificial intelligence in the pharma and biotech industries. But they focus on services, software licensing, and R&D partnerships. 

Two other broad categories of AI startups in the pharmaceutical industry include those in the drug repurposing space, like Healx and Lantern Pharma, and AI vendors in the clinical trial space, like Medidata

In this report, we are exclusively focused on the companies that can design and advance drug candidates de novo (mostly, small molecules), and have internal pipelines. 


A Wave of AI-designed Drug Candidates Hits The Breakwater

While hundreds of AI startups were founded since 2012, and raced towards building various systems for drug discovery, the wave of first preclinical and clinical candidates out of those efforts came later. 

For instance, Insilico made first headlines in 2019, having reached a notable proof-of-concept milestone where they predicted a molecule for a well-known target called DDR1 in just 21 days — and successfully validated prediction in vitro and in vivo. 

In the same year, Toronto-based Deep Genomics announced “industry’s first AI-discovered therapeutic candidate” DG12P1, for Wilson disease. Deep Genomics’s AI platform pinpointed a specific genetic mutation and helped design a compound to correct it, all within 18 months.



Around 2020-2022, a series of preclinical candidate nominations included AI-discovered targets and molecules by BenevolentAI, Exscientia, Insilico Medicine, Deep Genomics, and others, and around 2022 several dozens of AI-generated molecules were in clinical trials. 

In order to comprehend the advance of multiple AI-discovered molecules over the years, we have conducted a historical pipeline analysis of some of the most developed AI in drug discovery platforms, including (alphabetically): 

BenevolentAI, Exscientia, Insitro, Insilico Medicine, Recursion Pharmaceuticals, Relay Therapeutics, Schrödinger, Verge Genomics, and Valo Health

We did not include partner programs in this analysis. 


Table 1   

DISCLAIMER: Historical data sourced from archived public sources, including Webarchive service (see references 1-25), the data is NOT provided by the companies, and may have evolved.

——— Program Ownership Target Indication 2019 2020 2021 2022 2023


BEN-8744 Whole PDE10 Ulcerative Colitis     Discovery Preclinical Phase 1
BEN-28010 Whole CHK1 Glioblastoma Multiforme     Discovery Preclinical Preclinical
BEN-34712 Whole RAR⍺β ALS       Discovery Preclinical
- Whole - Parkinson's disease       Discovery Discovery
- Whole - Fibrosis       Discovery Discovery
Partnered Program Co-owner w/ AstraZeneca - Chronic Kidney Disease         Discovery
- Co-owner w/ AstraZeneca - Idiopathic Pulmonary Fibrosis         Discovery
- Co-owner w/ Merck - Oncology         Discovery
- Co-owner w/ Merck - Neurology         Discovery
- Co-owner w/ Merck - Immunology         Discovery
BEN-2293 Whole TrkA, TrkB, and TrkC Atopic Dermatitis Discovery Preclinical Phase 1 Phase 2 Phase 2`
BEN-9160 Whole Bcr-Abl ALS     Discovery Preclinical unknown
- Whole - Inflammatory bowl disease (IBD)       Discovery unknown
- Whole - Antiviral       Discovery unknown
- Whole - Oncology       Discovery unknown
- Whole - Oncology       Discovery unknown
- Whole - NASH       Discovery unknown
- Whole - Oncology       Discovery unknown
- Whole - Parkinson's disease       Discovery unknown
- Whole - Inflammation       Discovery unknown


EXS21546 Majority, w/ Evotec A2aR High Adenosine Signature Cancers   Preclinical Phase 1 Phase 1/2 Phase 1/2`
- Whole HPK1 Immuno-Oncology     Discovery unknown  
EXS74539 Whole LSD1 Oncology, AML, SCLC     Discovery Discovery Preclinical
EXS73565 Whole MALT1 Oncology, Hematology     Discovery Discovery Preclinical
- Whole - Oncology     Discovery unknown  
- Whole - Oncology     Discovery unknown  
- Whole - Oncology     Discovery unknown  
- Whole - Oncology     Discovery unknown  
- Whole Mpro COVID-19     Discovery Preclinical unknown
- Whole - Anti-infective     Discovery unknown  
- Whole NLRP3 Inflammation and Immunity     Discovery Preclinical unknown
GTAEXS617 Co-owner w/ Apeiron CDK7 Transcriptionally addicted cancers     Preclinical Preclinical Phase 1/2
EXS4318 Out-licensed, BMS PKC-theta inflammatory and immunologic diseases   Preclinical Preclinical Preclinical Phase 1
- Co-owner - Oncology     Discovery Discovery unknown
- Co-owner ENPP1 Oncology     Discovery Preclinical unknown
- Co-owner ENPP1 HPP     Discovery Preclinical unknown
- Co-owner - Inframmation and immunity     Discovery Discovery unknown
- Co-owner - Inflammation and Immunity     Discovery Discovery unknown
- Co-owner - Oncology     Discovery Discovery unknown
- Co-owner - Oncology     Discovery Discovery unknown
- Co-owner - Psychiatry     Discovery Preclinical unknown

Insilico Medicine

INS018_055 Whole TNIK IPF   Discovery Preclinical Phase 1 Phase 2
- Whole TNIK Kidney fibrosis     Discovery Preclinical Preclinical
- - TNIK Skin Fibrosis     Discovery Discovery unknown
- Whole TNIK IPF (inhalable)     Discovery Discovery Preclinical
ISM012 Whole PHD1/2 Anemia of Chronic Kedney Disease     Discovery Preclinical Phase 1
- Whole PHD1/2 Infammatory bowl disease (IBD)     Discovery Preclinical Phase 1
ISM8207 Co-owner w/ Fosun QPCTL Immuno-oncology     Discovery Preclinical Phase 1
- Co-owner w/ Fosun - Diabetic neuphropathy, FSGS     Discovery unknown  
ISM3312 - 3CLpro COVID-19     Discovery Preclinical Phase 1
ISM3412 Whole MAT2A MTAP-/-cancer     Discovery Preclinical Preclinical
ISM9274 Whole CDK12/13 Solid tumors     Discovery Discovery Preclinical
ISM027 Whole cMYC Solid tumors         Discovery
ISM022 Whole CDK8 AML, Solid tumors     Discovery Discovery unknown
ISM023 Whole PARP7 Solid tumors     Discovery Discovery unknown
ISM3091 Out-licensed, Exelixis USP1 BRCA-mutant cancer     Discovery Preclinical Phase 1
ISM5939 Whole ENPP1 solid tumors     Discovery Discovery Preclinical
ISM5043 Out-licensed, Menarini KAT6 ER+/HER2-breast cancer         Preclinical
ISM4525 Whole DGKA Solid tumors         Preclinical
ISM8001 Whole FGFR2/3 Solid tumors         Preclinical
ISM016 Whole NLRP3 Gout flare     Discovery Discovery Discovery
ISM6331 Whole TEAD Solid tumors         Preclinical

Recursion Pharmaceuticals

REC-4881 Whole MEK1 and MEK2 Familial Adenomatous Polyposis   Preclinical Phase 1 Phase 1 Phase 2
REC-3599 Whole PKC and GSK3ß GM2 Gangliosidosis   Preclinical Phase 1 terminated  
REC-2282 Whole HDAC Neurofibromatosis Type 2   Preclinical Phase 1 Phase 1 Phase 3
REC-994 Whole antioxidant, no specific target Cerebral Cavemous Malformation   Preclinical Phase 1 Phase 1 Phase 2
REC-3964 Whole - Clostridium Difficile Colitis   Discovery Preclinical Preclinical Phase 1
- Whole - Neuroinflammation     Discovery Discovery unknown
- Whole - Batten Disease     Discovery unknown  
- Whole - Charcot-Marie-Tooth Disease Type 2     Discovery Discovery unknown
- Whole - Immune Checkpoint resistance in STK11-NSCLC     Preclinical Preclinical unknown
- Whole - Oncoloty     Discovery Discovery Discovery
25 programs - - Various     Preclinical unknown  
REC-4881 Whole - AXIN1 or APC Mutant Cancers         Phase 1
- - - Pulmonary Arterial Hypertension       Preclinical unknown
- Whole - -       Preclinical unknown
Immunotherapy Target Alpha Whole - Oncology       Discovery Discovery
Immunotherapy Target Beta Whole - Oncology       Discovery unknown
- Whole - Hepatocellular Carcinoma       Discovery unknown
- Whole RBM39 HR-proficient Ovarian Cancer RBM39         Preclinical
Immunotherapy Target Delta Whole - -         Preclinical

Relay Therapeutics

RLY-4008 Whole FGFR2 (mutant+WT) FGFR2-altered cholangiocarcinoma (CCA) Discovery Phase 1 Phase 1 Phase 1 Phase 1/2
RLV-PI3K1047 (RLY-5836) Whole PI3Kα -     Discovery Preclinical Phase 1
RLY2608 Whole PI3Kα solid tumors with a PI3Kα mutation       Phase 1 Phase 1
- Whole PI3Kα -       Discovery unknown
RLY-2139 Whole CDK2 Oncology     Discovery Discovery unknown
- Whole ERα -         unknown
GDC-1971 Co-owner w/ Genentech SHP2 Cancers, expand into multiple combination Preclinical Phase 1 Phase 1 Phase 1 Phase 1
- Whole - Oncology     Discovery Discovery unknown
- Whole - Oncology     Discovery Discovery unknown
- Whole - Genetic disease     Discovery Discovery unknown
- Whole - Genetic disease     Discovery Discovery unknown


SDGR3 (SGR-1505) Whole MALT1 Relapsed or refractory B-cell lymphoma, chronic lymphocytic leukemia Discovery Discovery Preclinical Phase 1 Phase 1
SDGR1 Whole CDC7 Esophagial and Lung Cancers, Discovery Discovery Discovery unknown  
SGR-2921 Whole CDC7 Hematological cancers and solid tumors       Preclinical Phase 1
SDGR2 Whole WEE1 Ovarian, Pancreatic, Breast and Lung Cancers Discovery Discovery Discovery unknown  
SGR-3515 Whole WEE1/MYT1 Solid tumors       Discovery Preclinical
SDGR4 Co-owner w/ BMS HIF-2a Renal Cell Carcinoma   Discovery Discovery unknown  
SDGR5 Co-owner w/ BMS SOS1 KRAS-driven Cancers   Discovery Discovery Discovery Preclinical
- Co-owner w/ BMS - Oncology, Immunology, Neurology     Discovery unknown  
- Co-owner w/ BMS - Neurology         Discovery
- Co-owner w/ BMS - immunology          
- Whole LRRK2 Neurology       Discovery Discovery
- Whole PRMT5-MTA Oncology       Discovery Discovery
- Whole EFGR(C797S) Oncology       Discovery Discovery
- Whole - Oncology       Discovery Discovery
- Whole - Oncology       Discovery Discovery
- Whole NLRP3 Immunology       Discovery Discovery
- Whole - Immunology       Discovery Discovery
- Co-owner w/ BMS - Oncology       Discovery unknown
- Co-owner w/ BMS - Oncology       Discovery unknown
- Co-owner w/ BMS - Immunology       Discovery unknown
- Co-owner w/ BMS - Oncology, Immunology, Neurology       Discovery Discovery
- Co-owner w/ Takeda - Oncology       Discovery Discovery
- Co-owner w/ Takeda TYK2 Psoriasis         Phase 2
- Co-owner w/ Zai Lab - Oncology       Discovery unknown
- Co-owner w/ Lilly - Immunology       Discovery Discovery
- Co-owner w/ Ajax JAK2 Oncology         Discovery
- Co-owner w/ Bright Angel Therapeutics HSP90 Antifungal         Discovery
- Co-owner w/ loxo Therapeutics undisclosed oncology         Phase 1
- Co-owner w/ Morphic Therapeutic α4β7 IBD         Phase 2
- Co-owner w/ Morphic Therapeutic α4β7 GI indications         Discovery
- Co-owner w/ Morphic Therapeutic αvβ8 Solid tumors, fibrosis         Discovery
- Co-owner w/ Morphic Therapeutic - Pulmonary arterial hypertension         Discovery
- Co-owner w/ NimbusTherapeutic HPK1 Immuno-oncology         Phase 1/2
- Co-owner w/ Otsuka - -         Discovery
- Co-owner w/ Sanofi - oncology         Discovery
- Co-owner w/ Structure Therapeutics APJR Pulmonary arterial hypertension         Phase 1
- Co-owner w/ Structure Therapeutics - -         Discovery
- Co-owner w/ Structure Therapeutics LPA1R Idiopathic pulmonary fibrosis         Preclinical


- Co-owner w/ BMS, TSC alliance - Genetic Epilepsies, ALS         Discovery
- Whole - Solid Tumors         Discovery
- Whole - MASLD, Obesity         Discovery

Verge Genomics

VRG50635 Whole PIKfyve ALS   Discovery Preclinical Phase 1 Phase 1/2
- Whole - Parkinson's Disease       Discovery Discovery
- Whole - Parkinson's Disease       Discovery unknown
- Whole - Parkinson's Disease       Discovery unknown
- Whole - Frontotemporal Dementia       Discovery unknown
- Whole - Progressive Supranuclear Palsy       Discovery unknown
- Whole - Schizophrenia       Discovery Discovery
- Whole - Neurodegenerative Diseases       Discovery Discovery
- Whole - Undisclosed       Discovery unknown
- Whole PIKfyve COVID-19     Discovery Preclinical unknown
- - - Psoriasis         Discovery
- - - Atopic Dermititis         Discovery
- - - Crohn's Disease         Discovery
- - - Ulcerative Colitis         Discovery
Partnered Programs Co-owner w/ Lilly - ALS         Discovery
- Co-owner w/ Alexion - Neurodegenerative Diseases         Discovery
- Co-owner w/ Alexion - Neuromuscular Diseases         Discovery

Valo Health

OPL-0301 - S1P1 agonist Heart failure and Acute Kidney Injury     Phase 1 Phase 2 Phase 2
OPL-0401 - ROCK 1/2 inhibitor Diabetic Retinopathy     Phase 1 Phase 2 Phase 2
OPAL-0022 - - Atherosclerosis     Discovery unknown  
OPAL-0004 - - Atherosclerosis, Giloblastoma     Discovery unknown  
OPAL-0018 - - Atherosclerosis     Discovery unknown  
OPAL-0003 - - Heart Failure, Giloblastoma     Discovery unknown  
OPL-0101 - - Immuno-Oncology   Discovery Preclinical unknown  
OPAL-0021 - - cancer     Discovery unknown  
OPAL-0015 - USP28 NSCLC, Squamous Cell Carcinoma, Targeted Defined Tumors     Discovery unknown  
OPAL-0024 - - Solid Tumors     Discovery unknown  
OPAL-0001 - PARP1 Medula/Glioblastoma Brain Tumors, Breast Cancer     Discovery unknown  
OPAL-0014 - - Pancreatic Ductal Adenocarcinoma (PDAC), Targeted Defined Tumors     Discovery unknown  
OPAL-0023 - - Defined Tumors, Immune Modulation     Discovery unknown  
OPAL-0012 - USP7 NSCLC     Discovery unknown  
OPAL-0016 - - Induced Neuropathy and Cardiomyopathy     Discovery unknown  
OPAL-0002 - - Neurodegenerative disorders     Discovery unknown  
OPAL-0006 - - Neurodegenerative: Oncology (metastatic)     Discovery unknown  

2023-present moment

In 2023, the first wave of AI-designed (or claimed so) drug candidates hit the breakwater. The industry recorded a number of clinical trial setbacks for some AI-designed drug candidates, including Exscientia's cancer drug candidate EXS21546 which was discontinued out of strategic pipeline prioritisation, as the company representatives explained in an email to BiopharmaTrend.

An AI-inspired schizophrenia drug candidate from partners Sumitomo Pharma and Otsuka Pharmaceutical failed to outperform a placebo in two Phase 3 studies. Sunovion, a subsidiary of Sumitomo Pharma, brought compounds to the alliance that were then screened using PsychoGenics’s SmartCube technology, which employs computer vision to analyze and define behaviors of mice treated with a potential drug.

Adding to negative statistics, BenevolentAI's lead drug BEN-2293 failed to beat a placebo in a Phase 2a atopic dermatitis study, leading to cutting up to 180 jobs and reorganizing its pipeline to conserve cash. The company’s valuation dropped significantly in 2022, has not recovered ever since, and is currently around $145 million (Table 2), a fraction of what it used to be in earlier years.  

Notwithstanding years in business and hundreds of millions in raised capital, some AI companies have not produced any clinical drug candidates yet. 

For instance, according to a recent Endpoints News interview with Dr. Abraham Heifets, Co-founder and CEO of Atomwise -- the AI in drug discovery pioneer founded in 2012 and raised over $170 million, the company had to cut ‘hundreds of AI discovery programs‘ and focus on just several assets for internal development recently. Currently, all its drug candidates are in preclinical stage. 

Founded in 2018, and led by a prominent deep learning scientist Dr. Daphne Koller, Insitro is another example of a company with a seemingly “relaxed” pace of advancing its internal drug candidates. After 6 years in the business, and $643 million of venture capital money raised, the company only has 3 drug candidates in the discovery stage (Table 1). 

That being said, Insitro has a strong portfolio of external deals with BMS, Gilead and others, and the company is heavily focused on modeling disease biology. So the limited internal pipeline can be explained by a different focus, and probably we may expect rapid portfolio expansion in the near time.  

On the other hand, the industry recorded distinct success stories with growing “AI-inspired” pipelines, like the one of Insilico Medicine, which managed to build an impressive clinical pipeline of 17 preclinical candidates in under 3 years. Several of those are now in clinical trials, including a recent phase 2 candidate for idiopathic pulmonary fibrosis (IPF), five phase 1 candidates for various indications, including kidney fibrosis, inflammatory bowel disease (IBD), immuno-oncology, and COVID-19, and around a dozen preclinical programs in late stages of development. 

It is noteworthy that the majority of Insilico Medicine’s AI-discovered programs are first-in-class drug candidates for high-novelty targets or best-in-class candidates for moderately novel targets. The company’s apparent ability to come up with novel targets consistently stems from its target discovery engine PandaOmics. 

Another leading player, Recursion Pharmaceuticals seems to be progressing consistently, overall, as the company embraced a robust multifaceted approach to building its end-to-end AI engine Operation System, and its high throughput biology experimentation facilities and robotized lines. 

The company has a strong pipeline of two Phase 1 and three Phase 2 candidates, two of which were in-licensed from previous developers. One more candidate did not progress beyond Phase 1 and was put on hold.

  It should be noted, however, that according to the Endpoints article, Recursion Pharmaceuticals have not delivered on the promise of ‘generating 100 drugs‘ using AI, as the company advertised a decade ago. It is illustrative of how challenging the drug discovery is, even with the cutting-edge AI technologies and R&D infrastructure. 

In 2023 Verge Genomics got positive safety and tolerability data from the Phase 1 clinical trial for its leading candidate VRG50635, a potential best-in-class therapeutic for all forms of ALS. Verge Genomics used CONVERGE™, the company’s all-in-human, AI-powered platform to develop its drug discovery program.

Also, the FDA's clearance of A2A Pharmaceuticals's Investigational New Drug (IND) application for A2A-252, a TACC3 protein-protein interaction (PPI) inhibitor, showcases the potential of AI in accelerating drug development. 

Utilizing its AI-driven SCULPT computational platform, A2A Pharmaceuticals, with a lean team of four and limited funding, managed to advance two clinical stage programs, including A2A-252.


AI in Drug Discovery Sector Keeps Consolidating

The AI in the drug discovery and development race has been going for more than a decade now, and today we see all signs of the sector’s consolidation. 

Over the last 6 years, there were more than 30 merger and acquisition events in this space. According to the BiopharmaTrend report some of the AI-focused deals in drug discovery, biotech, and clinical trials space included: the 2018 acquisition of Flatiron Health by Roche for $1.9 billion; the 2019 acquisition of Numerate by Valo Health; the 2020 acquisition of Haystack Biosciences by Insitro; the 2021 acquisition of Prescient Design by Genentech; the 2022 acquisitions of Zymergen by Ginkgo Bioworks for $300 million; the 2023 acquisition of InstaDeep by BioNtech for $549 million and double acquisition of Cyclica and Valence Labs by Recursion Pharmaceuticals for around $90 million combined -- to name just a few. 

Some of the notable deals are summarized in the figure below:

AI in Drug Discovery Sector Consolidation

Another driver of consolidation is expansion of “big tech” giants into the life sciences, including NVIDIA, Alphabet, Microsoft and others. They leverage their cutting edge resources in artificial intelligence research, world-class tech infrastructures (software, cloud, computational power) and flexible business models. 

Famous successes of Alphabet subsidiary DeepMind in protein modeling space, Isomorphic Labs’ recent partnerships with Eli Lilly and Novartis, the advent of NVIDIA’s AI-based Clara Discovery platform for everything from drug design to healthcare research, and Microsoft Cloud for life sciences.

As Dr. Alex Zhavoronkov writes in his Forbes article: “In 2024, I predict NVIDIA will come out big with its healthcare platform so that the need for new AI in drug discovery (AIDD) companies will go away. Drug discovery players will be able to use NVIDIA tools in their cloud, on Amazon, or on massive clusters of NVIDIA GPUs locally. 

It is unlikely to hurt established AIDD players with end-to-end AIDD platforms and significant validation, but it will inhibit the formation of startups with little or now differentiation, and it will add more credibility to the field.”  

Dr. Zhavoronkov continues about Microsoft: “In 2024, we should expect to see massive growth in LLMs in pharma and biotech, with Microsoft being the main provider. Even when some LLM startups achieve superior performance in benchmarks and come up with new models, they do not stand a chance in big pharma. 

Pharma is very conservative - there are too many compliance and legal challenges to deploying generative AI from a new vendor. Microsoft made it easy to use the latest OpenAI models on Azure Cloud and to implement sophisticated AI platform architectures. And Microsoft works all over the world. I predict many internal-external LLM architectures will be built on the Microsoft platform”.

Next, according to the BiopharmaTrend report, venture capital firms are increasingly prioritizing smaller numbers of established AI in drug discovery players over new platform companies, which is a sign of consolidation. 

Finally, ‘big pharma’ and leading biotech corporations are increasingly shifting from external partnerships towards building their own AI muscle, effectively diminishing opportunities for novel startups to win R&D dollars.

Big pharma is increasingly choosing well established technology vendors and validated AI for drug discovery engines. 

The consolidation of the AI in the drug discovery sector naturally led to formation of a distinct group of AI frontrunners -- companies that managed to build sufficient AI capabilities, robust business models, and win major clients. From the Table 2 below one can see that several companies, including Insilico Medicine, Recursion Pharmaceuticals, Schodinger, and XtalPi demonstrate strong financials, compared to some of the peers in this space: 


Table 2 **
  BenevolentAI *** Exscientia *** Insilico Recursion Pharmaceuticals Relay Therapeutics Schrodinger XtalPi **`
Year 2021 2022 2023 2021 2022 2023 2021 2022 2023 2021 2022 2023 2021 2022 2023 2021 2022 2023 2021 2022 2023Q2
Total Revenue 6,254 12,774 9,334 36,995 32,932 25,565 4,713 30,147 51,180 10,178 39,843 44,575 3,029 1,381 25,546 137,931 180,955 216,666 9,878 19,336 11,299
Drug discovery services             3,687 28,648 47,818             24,695 45,377 57,542 6,189 12,712 5,100
Software solution services             1,026 1,499 3,362             113,236 135,578 159,124 3,689 6,625 6,199
Total Revenue Growth, %   104% -27%   -11% -22%   540% 70%   291% 12%   -54% 1750%   31% 20%   112%  
Drug discovery services Growth, %               677% 67%               84% 27%   123%  
Software solution services Growth, %               46% 124%               20% 17%   95%  
Research and development expenses -76,962 -86,958 -77,380 -59,560 -155,888 -163,535 -38,489 -78,175 -97,341 -135,271 -155,696 -241,226 -172,650 -246,355 -330,018 -90,904 -126,372 -181,766 -33,442 -52,048 -33,124
Operating Loss -164,052 -238,352 -98,766 -74,230 -175,759 -224,088 -49,359 -83,616   -182,775 -245,727 -350,060 -364,698 -299,275 -373,000 -111,443 -146,817 -177,448 -47,101 -76,171 -61,464
Total current assets 76,567 183,977 116,355 786,694 670,204 515,567 161,541 218,751 188,653 534,718 569,814 438,137 976,242 1,019,505 770,103 625,060 533,989 567,796 616,547 521,516 456,362
IPO Date 2021/12 2021/9   2021/4 2020/7 2020/2  
Post-money valuation of last financing round     894.7M       1,968M
Valuation (As of Mar 26th 2024) 93M 749M   2,422M 993M 1,845M  
Biological ability Partnerships Partnerships External user base Partnerships      
Chemistry ability Partnerships Partnerships External user base   Partnerships External user base External user base
Clinical ability     External user base        
Therapeutic Area       Rare & Other, Oncology Targeted Oncology, Genetic Disease    
Phase II 0 1 1 3 1 0 0
Phase I 1 0 5 2 2 2 0
IND enabling 2 2 9 2 1 2 3

** - the data taken from annual reports, see references 26-40

*** - for comparison reasons, values in the UK companies reports (originally in GBP) have been converted to USD at the GBP/USD rate (closing price) as of 31 December of the year depending on the report:
2021 - 1.3522; 2022 - 1.2097; 2023 - 1.2732

**` - for comparison reasons, values in the XtalPi report (originally in RMB) have been converted to USD at the RMB/USD rate (closing price) as of 31 December of the year depending on the report:
2021 - 0.1573; 2022 - 0.145; 2023 - 0.1413

Table 3 ``
Company Short % of Shares Current Price (USD)
(US Trading Hour)
52-Week Price Change % Current MarketCap (USD'MM) Last 12 months'
Operating Cash Flow (USD'MM)
Last 12 months'
Revenue (USD'MM)
3 year's Revenue Trend
Recursion Pharmaceuticals, Inc 18.25%
10.32 64.02% 2,422
Schrodinger, Inc. 7.48%
25.65 5.07% 1,845
Relay Therapeutics, Inc. 10.79%
7.57 -49.80% 993
Exscientia plc 5.42%
5.74 9.19% 749
BenevolentAI N/A 0.73 -70.25% 93
Insilico Medicine N/A N/A N/A 895

`` - data taken from Yahoo Finance for March 26, 2024.

It is worth noting that XtalPi seems focused exclusively on the contract research business model. At least, the company removed its pipeline information from the english version of its website. 


Looking into the future

The AI in the drug discovery space is getting consolidated with the rising barriers of entry for new startups, but also more opportunities for those newcomers that do manage to innovate efficiently, raise money, and leverage novel technologies. 


Big tech enters the game

A growing influence of large technology vendors, like Microsoft, Alphabet and NVIDIA can be expected, and they will be increasingly serving as gravitation points for novel startups. For instance, NVIDIA’s Inception program already now nurtures over 1,800 healthcare startups developing cutting-edge, GPU-based tools to optimize operations, enhance diagnostics and develop novel therapeutics.

In 2024, the pharmaceutical and biotechnology sectors are anticipated to significantly expand their adoption of large language models (LLMs), predominantly through Microsoft's Azure Cloud, leveraging OpenAI's models for AI-powered drug discovery (AIDD). Despite the emergence of startups with potentially superior LLMs, the stringent compliance and legal frameworks within the pharma industry favor established providers like Microsoft, which offers comprehensive support for deploying generative AI globally. The trend towards consolidation in the AIDD market is expected to intensify, with a few well-established entities dominating, thanks to their validated platforms and the industry's cautious approach towards adopting innovations from newer vendors.


Era of AI pilots with “big pharma” is over

Big pharma has grown AI muscle to the point that they can oftentimes innovate internally, and depend less on the external pilot programs. The era of numerous AI-deals with everyone around is over. In 2024 and beyond, we may expect an increasing amount of strategic partnerships between big pharma and the most established AI in drug discovery platforms.

Such partnerships will be growing in scope and size of total remuneration. 

Already now some deals include not only promise of milestone future payments and royalty payments, but also sizable upfront payments. 

In September 2023, Insilico Medicine sold rights to develop and sell a potential cancer drug to California-based Exelixis Inc. for an upfront payment of $80 million and is co-developing another candidate for cancer with Shanghai Fosun Pharmaceutical Group Co., controlled by Chinese billionaire Guo Guangchang.

Around the same time, Exscientia collaborated with Merck KGaA, focusing on AI-driven drug discovery in oncology, neuroinflammation, and immunology, with a $20 million upfront payment and potential milestones up to $674M plus royalties on sales for developed therapies.


AI validation in clinical trials

As more drug candidates from established AI in drug discovery players will be progressing along the clinical trial pipelines, we will have meaningful insights into how AI is really changing the quality of innovation. It is clear that AI can accelerate research and make it cheaper. But does it increase the probability of success? This is yet to be understood in more detail. 

At the same time, it is important to mention that while the role of AI in changing the paradigm of early drug discovery is still a matter of validation and more data, the role of AI in drug development is already felt. 

In a recent video interview with CNBC, Vas Narasimhan, CEO of Novartis, shares his vision about AI in drug discovery. He thinks it won’t impact early stages of drug discovery within the next five years (we do not quite share this opinion, based on our analysis of several leading companies, such as Insilico Medicine, Recursion Pharmaceuticals, and Schrodinger).

However Vas points out that AI has already impacted drug development and clinical research: e.g.: new trial protocols, working with regulators, working with large patients datasets, etc.

He estimates that AI already helps save 6-9 months of drug development, which can translate into notable financial savings and acceleration.


Data is new priority in the AI race

Finally, the leading AI companies have come to understand the critical importance of generating and possessing their own unique biological data. This realization marks a pivotal shift in the industry's approach to leveraging artificial intelligence for drug development.

It is not enough any longer to have cutting-edge models and algorithms, no matter how sophisticated. It becomes a matter of strategic differentiation to be able to generate or otherwise acquire and control big datasets for model training (e.g. omics, imaging, EHR, etc). 

Some companies, like Moderna, Recursion Pharmaceuticals and XtalPi, naturally evolved as digital biotechs with AI-controlled robotized high throughput facilities for running preclinical experiments at scale. 

Insilico Medicine is a good example of an AI in drug discovery company that initially evolved as an algorithm-centric company with an efficient collaboration structure. But in the recent couple of years, the company focused massively on augmenting its capability with high throughput experimental capacity of its own, and in 2023, Insilico Medicine launched a 6th generation Intelligent robotics drug discovery laboratory, located in Suzhou BioBAY Industrial Park, China.

Exscientia is yet another example of this trend, as the company opened a facility in Oxfordshire, England, for AI and robotics integration in chemical synthesis and closed-loop drug design, aiming to minimize human intervention and accelerate experimental processes. 

Concurrently, the ethical use of AI gains prominence, with international efforts to establish guidelines following the European Union directive and a US executive order, addressing security risks and promoting responsible AI development in the wake of increasing accessibility to generative AI models.


Report methodology

The historical data analysis of therapeutic pipelines (Table 1) has been performed based on the available website snapshots from Web Archive, where we were able to analyze how pipeline diagrams looked in previous periods. In some cases, yearly financial reports were used to get pipeline information for previous years. 

The effort was made to track the progression of each molecule or program in any given pipeline over the years, and if the program was unavailable in the next year, it was generally assumed that the program was put on hold for various reasons. 

Financial data for Table 2 was obtained from relevant annual reports, in the case of publicly traded companies, and from other publicly available sources for private companies. 


Correction policy

In our report on comparing 10 companies, including those that are publicly traded, we made every effort to ensure accuracy and transparency. 

Should any factual inaccuracies or outdated information be identified, we will promptly issue corrections in a dedicated section of our report. This correction policy applies to financial data, company profiles, and any comparative analysis presented. Stakeholders are encouraged to report potential inaccuracies to our editorial team via our official contact channels. Corrections will be clearly dated and described, maintaining the integrity of our comparative report and ensuring our readers have access to the most current and accurate information.



1. Schrodinger, pipeline, December 2023

2. Schrodinger, pipeline, November 2022

3. Schrodinger, pipeline, June 2021

4. Schrodinger, pipeline, June 2020

5. Schrodinger, pipeline, Juy 2019

6. Recursion, pipeline, December 2023

7. Recursion, pipeline, January 2022

8. Recursion, pipeline, February 2021

9. Recursion, pipeline, January 2021

10. Exscientia, pipeline, November 2023

11. Exscientia, PR, August 2022

12. Exscientia, article, July 2022

13. Exscientia, annual report, 2021

14. Relay, pipeline, November 2023

15. Relay, annual report (PDF), 2022

16. Relay, annual report (PDF), 2021

17. Relay, annual report (PDF), 2020

18. BenevolentAI, pipeline, December 2023

19. BenevolentAI, annual report (PDF), 2022

20. Insilico, pipeline, December 2023

21. Insilico, pipeline, October 2022

22. Insilico, pipeline, February 2022

23. Verge Genomics, pipeline, February 2024

24. Verge Genomics, pipeline, November 2022

25. BenevolentAI, report release, 2021 and 2022

26. BenevolentAI, report release, 2023

27. Exscientia, annual report (PDF), 2022

28. Exscientia, annual report (PDF), 2023

29. Recursion, annual report (HTML), 2021

30. Recursion, annual report (HTML), 2022

31. Recursion, annual report (HTML), 2023

32. Relay, annual report (HTML), 2021

33. Relay, annual report (HTML), 2022

34. Relay, annual report (HTML), 2023

35. Schrodinger, annual report (PDF), 2021

36. Schrodinger, annual report (PDF), 2022

37. Schrodinger, annual report (PDF), 2023

38. XtalPi, report, 2021, 2022, 2023Q2

39. Insilico, annual report (PDF), 2023

About Us

We have been tracking the adoption of artificial intelligence in the pharmaceutical and biotech industries since 2016.

Having accumulated extensive knowledge about this industry sector, we have become a reputable agency offering strategic consulting and market research services.

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