Active learning in drug discovery
Workshop, Online, 2022
Active learning and Bayesian optimization have become important/prevalent tools in drug discovery and design. They provide a systematic way to select samples with a high amount of information. This property has made them an ideal choice in early-stage drug discovery, e.g., in hit identification through virtual screening and also lead optimization. Also, they can be used to search through usually very large drug combination space to find drug pairs with the highest synergy score. In this workshop, first, we read and review the theoretical aspects of active learning and bayesian optimization.