BIO 2025

We Still Need the People: AI/ML Drug Discovery is Here to Stay, but we Could be its Rate Limiting Factor

Panel Participant:
Jenny Chambers, Senior Director of Education, Schrödinger

Abstract:
Computational methods including AI/machine learning have the potential to be transformational in biopharma by accelerating and enhancing many aspects of drug discovery to bring better drug candidates with a higher likelihood of success to the clinic. Robust data sets are often cited as the limiting factor for this technology. However, less discussed but crucial to the success of computational drug discovery is fostering a new generation of drug hunters with multi-disciplinary training needed to make the best use of these advancements. There may be a shortage of computational chemists and molecular modelers needed to explore the vast array of opportunities that can benefit from computational drug discovery. Hear from a panel of academic and industry leaders that are developing this next-generation, what is most important for them and what the broader ecosystem can do to help fill in the pipeline gaps and ensure we have the people in place to match the technology. This session will focus on the benefits of computational methods, including AI/machine learning, to advance drug discovery, as well as the importance of fostering the next generation of scientists leveraging these vast datasets.