Back to the future of lead optimization: benchmarking compound prioritization strategies

Abstract

Drug discovery requires traversing vast chemical spaces to identify compounds exhibiting favorable potency, selectivity and absorption–distribution–metabolism–excretion–toxicity (ADMET) profiles. During this process, the synthetic and assay throughput is generally markedly lower than the ideation of new propositions by the project team members, so that the prioritization of new syntheses is indispensable. We herein introduce a framework for simulating the outcome of multi-objective prioritization strategies during lead optimization. Based on the Design–Make–Test–Analyze (DMTA) paradigm, historical discovery programs are replayed round by round using user-defined compound selection strategies. We develop qualitative and quantitative tools to assess their performance in retrieving the best compounds and exploring the project's chemical space. We demonstrate our pipeline using four industrial datasets, each containing chemical structures, assay values and time stamps. Multiple selection strategies are implemented, including approaches inspired by active learning (AL), multi-criteria decision analysis (MCDA), and medicinal chemistry heuristics, that display distinct behavior in the selection of compounds. Retrospective analysis provides a rigorous, low-cost test bed for investigating selection strategies in lead optimization and could help reduce the cost, duration and risk of lead optimization projects.