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By PharmaCompass
2025-03-13
Impressions: 406
Artificial intelligence (AI) is emerging as a transformative force in drug discovery and development. The global AI in drug discovery market was valued at US$ 1.99 billion in 2024 and is estimated to reach US$ 2.65 billion in 2025. Going forward, the market is expected to grow at an impressive compounded annual growth rate (CAGR) of 29.6 percent, to reach a market size of US$ 35.42 billion by 2034, says a report by Polaris Market Research.
This growth expectation mirrors the eagerness within the biopharma industry to leverage AI in order to overcome the traditional barriers of lengthy timelines, exorbitant costs, and high failure rates. This eagerness is also being manifested in deal making — Big Pharma has been busy forging alliances with AI-driven drug discovery companies. Over the last two years, almost every big drugmaker, including Novo Nordisk, Merck, BMS, Pfizer, AstraZeneca, Otsuka Pharmaceutical, Novartis and Sanofi, has signed deals with various AI drug discovery companies.
PharmaCompass' compilation shows there are scores of molecules discovered by AI currently in discovery and developmental phases, demonstrating the technology's growing impact on pharmaceutical pipelines.
Access the Dashboard on AI-Discovered Drug Candidates (Free Excel Available)
Novo, Lilly, BMS invest in AI partnerships; Recursion, Exscientia merge to create AI powerhouse
With the transformative potential of AI becoming increasingly evident, we witnessed a slew of deals in the biopharma industry over the last six months. A significant deal in the AI drug discovery space was the merger of two leaders — Recursion and Exscientia — that took place in November last year. Exscientia is now a wholly-owned subsidiary of Recursion.
The merger has created a global technology-enabled drug discovery leader with end-to-end capabilities. The merged entity has over 10 clinical and preclinical programs, and around 10 advanced discovery programs in the pipeline.
Apart from this merger, several large drugmakers have struck deals with AI drug discovery firms since September last year. For instance, in January this year, Valo Health and Novo Nordisk expanded their collaboration to discover and develop novel treatments for cardiometabolic diseases. This deal, valued at up to US$ 4.8 billion, aims to harness the power of AI and extensive human datasets. Building on their initial 2023 partnership, it will enable the development of up to 20 drug programs.
Similarly, Pfizer, which is already big on AI, extended its AI drug discovery pact with PostEra and also partnered IgnitionAI to enhance drug discovery — all within the last six months. And Novartis struck a drug discovery deal with Generate BioMedicines in September.
Insilico Medicine has been actively forging partnerships. In January, Insilico entered a second exclusive global license agreement with Menarini Group for an AI-discovered preclinical asset targeting high unmet needs in oncology. Insilico also has a tie-up with Sanofi, dating back to 2022.
In December 2024, AI Proteins announced a research collaboration and option agreement with BMS to design and optimize mini-proteins for therapeutic use. And in September, Eli Lilly joined hands with Genetic Leap to accelerate the development of genetic medicines. Lilly will leverage Genetic Leap’s RNA-targeted AI platform to generate oligonucleotide drugs against selected targets.
Access the Dashboard on AI-Discovered Drug Candidates (Free Excel Available)
AI leads to faster target identification; Insilico, BenevolentAI’s candidates in trials
Computational biology, and especially AI, has fundamentally changed the drug discovery paradigm. The traditional method of drug discovery used to take 13 to 15 years, costing an average of US$ 2.5 billion in investment before a drug got approved and launched in the market. Additionally, less than 10 percent of candidates in phase 1 clinical trials used to end up getting approved by the US Food and Drug Administration (FDA). All that has begun to change.
According to Khair ElZarrad, Director of the Office of Medical Policy within FDA’s Center for Drug Evaluation and Research, since 2016, FDA has received approximately 300 submissions that reference AI use as of May 2024. These submissions range from drug development, drug discovery, clinical research, clinical trials as well as post-market safety surveillance and manufacturing.
Though we are yet to witness an AI-generated candidate translate into an approved therapy, target identification in drug discovery is visibly becoming a lot faster. This is the process that identifies specific molecules or pathways in the human body that are linked to a disease.
The best example of dramatic acceleration of timelines is Insilico Medicine’s development of INS018_055, a candidate for idiopathic pulmonary fibrosis that entered phase 2 trials in June 2023. Using their AI platform PandaOmics, researchers at Insilico compressed target identification from years to just 18 months.
Similarly, BenevolentAI successfully employed their AI platform to identify a new target for ulcerative colitis, leading to the development of BEN-8744, a candidate that has been in phase 1a clinical trials since August 2023.
Access the Dashboard on AI-Discovered Drug Candidates (Free Excel Available)
Oncology leads AI drug discovery market; Recursion shows how to repurpose drugs using AI
The integration of AI in oncology research has been impactful, accounting for the largest revenue share (about 22.4 percent) in the AI drug discovery market in 2023. AI technologies have accelerated the discovery and development of new cancer treatments, bolstered personalized medicine approaches, and improved clinical trial designs in the arena of this complex disease.
Another promising application is drug repurposing, where AI can identify new therapeutic applications for existing drugs. Recursion Pharmaceuticals’ REC-2282 exemplifies this approach. This therapy is currently in phase 2/3 studies for neurofibromatosis type 2 (a genetic condition that causes tumors to grow along nerves). By repurposing compounds already vetted for safety, this strategy can bypass early-stage failures, reducing both time and cost compared to developing entirely new molecules.
However, the use of AI in drug discovery is not without challenges. Exscientia’s EXS-21546, designed to treat solid tumors, entered phase 1/2 trials in 2020 but was discontinued in 2023 after modeling suggested that achieving a suitable therapeutic index would be difficult. Though this is a clinical failure, it highlights AI’s value in identifying potential failures earlier on in the development process, saving resources that would have otherwise been spent on later-stage trials.
Access the Dashboard on AI-Discovered Drug Candidates (Free Excel Available)
Our view
The Covid-19 pandemic was a time when humanity felt the urgent need to speed up the process of drug discovery, testing and approvals. It also hastened the process of integrating AI into pharmaceutical research, leading to the launch of Covid vaccines in record time. Today, there is no looking back — AI is bringing about a paradigm shift in how drugs are discovered. With increasing reliance on AI, there is hope that drug discovery will be more accurate, efficient and less time and cost consuming.The PharmaCompass Newsletter – Sign Up, Stay Ahead
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Image Credit : AI REVOLUTIONIZING DRUG DISCOVERY by PharmaCompass license under CC BY 2.0
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