Djork-Arne Clevert

Machine learning (ML) is driving exciting innovations in drug discovery, but we need to be mindful of the circumstances that set this application apart. Unlike other fields with fit-for-purpose datasets consisting of millions of examples, published datasets in drug discovery are classically heterogeneous, imbalanced, noisy and expensive to generate1.

Abstract The Structural Genomics Consortium is an international open science research organization with a focus on accelerating early-stage drug discovery, namely hit discovery and optimization. We, as many others, believe that artificial intelligence (AI) is poised to be a main accelerator in the field. The question is then how to best benefit from recent advances in AI and how to generate, format and disseminate data to enable future breakthroughs in AI-guided drug discovery.