Since 2021, approximately $26.6 billion* in venture capital has been invested in AI-driven biotech companies, out of a total $109.3 billion* allocated to the biotech sector during the same period. This indicates that over 25% of biotech venture capital funding has been directed toward AI-driven approaches in the past four years.
In an article published in June 2024 in Drug Discovery Today, Jayatunga et al. analyzed the clinical pipeline of AI-discovered drugs currently in development. Notably, they conducted an in-depth examination to identify the specific applications of AI employed in the discovery of each drug candidate (see figure attached).
Interestingly, authors have listed 300 AI-native biotechs active worldwide with 67 drugs in clinical development among which 24 AI-discovered targets and only 3 to 9 AI-discovered novel targets.
Compared to the 6,147** drugs currently in clinical development, TechBio remains a niche :
The analysis presented suggests that, in Phase I trials, AI-derived molecules can have a success rate of 80–90%, which is substantially higher success rates than historic averages. In Phase II trials, our data indicate a success rate of AI-discovered molecules of 40%, which is in line with historic industry averages.
According to the authors, a compelling picture emerges: by integrating observed Phase I and II success rates for AI-discovered drugs with historical Phase III success rates, the overall probability of a molecule advancing through all clinical phases increases from 5–10% to approximately 9–18%. This represents a near doubling of pharmaceutical R&D productivity, highlighting the substantial potential gains offered by AI-driven drug discovery.
*Nature – Biotech Financing, Aug 8th 2024, Darkest Before The Dawn
**IQVIA, dec 2022
For more information about this publication, please check : Kp Jayatunga M, Ayers M, Bruens L, Jayanth D, Meier C. How successful are AI-discovered drugs in clinical trials? A first analysis and emerging lessons. Drug Discov Today. 2024 Jun;29(6):104009. doi: 10.1016/j.drudis.2024.104009. Epub 2024 Apr 30. PMID: 38692505.