CTAD Lessons for 2020: More Phase 2 Trials, More Diversity


-Funding agencies encourage exploration of more diverse therapeutic targets. -A return to getting efficacy data in Phase 2 may lower failure rate in Phase 3. -Machine learning could help slim down and streamline trials.


186 Startups Using Artificial Intelligence in Drug Discovery

Simon Smith for BenchSci

Welcome to what I hope is a comprehensive list of startups using machine learning to research and develop drugs. I began this list in November 2017. At that time, it had 37 startups. I have updated it continuously since then. My focus is companies that (1) are new (I'm leaving this purposely vague, but typically founded post-2012); (2) use AI as a key differentiator; (3) develop products or drugs (I exclude service businesses); and (4) have demonstrated traction, such as via at least seed funding.


AI, machine learning offer hope for speeding Alzheimer’s development

Brian Orelli for BioWorld

Machine learning and artificial intelligence (AI) are already being actively used in drug discovery to evaluate potential binding of small-molecule drugs to proteins, but there's potential for the technologies to be used on the development side as well, especially in hard-to-treat diseases such as Alzheimer's disease.


Unlearn.AI Announces Results Generated from First-of-its-Kind Machine Learning Platform to Accelerate Alzheimer’s Drug Development


Technology creates Digital Twins and Intelligent Control Arms to reduce clinical trial timelines and improve confidence in trial results