Active Learning on Synthons for Molecular Design

Exhaustive virtual screening is highly informative but often intractable against the expensive objective functions involved in modern drug discovery. This problem is exacerbated in combinatorial contexts such as multi-vector expansion, where molecular spaces can quickly become ultra-large. Here, we introduce Scalable Active Learning via Synthon Acquisition (SALSA): a simple algorithm applicable to multi-vector expansion which extends pool-based active learning to non-enumerable spaces by factoring modeling and acquisition over synthon or fragment choices. Through experiments on ligand-and structure-based objectives, we highlight SALSA's sample efficiency, and its ability to scale to spaces of trillions of compounds. Further, we demonstrate application toward multi-parameter objective design tasks on three protein targets - finding SALSA-generated molecules have comparable chemical property profiles to known bioactives, and exhibit greater diversity and higher scores over an industry-leading generative approach. Given the strong association between a molecule's core scaffold and its chemical properties, a common workflow is to iteratively design, make, and test changes at targeted R-groups in order to advance therapeutics through the discovery pipeline (Schneider, 2017). Exhaustive virtual screening of R-group changes aids designers and medicinal chemists in the search for promising, synthesizable molecular structures, but quickly becomes intractable against computationally expensive scores as the number of possible attachments increases.