Descriptor-free molecular discovery in large libraries by adaptive substituent reordering

Abstract

Molecular discovery often involves identification of the best functional groups (substituents) on a scaffold. When multiple substitution sites are present, the number of possible substituent combinations can be very large. This article introduces a strategy for efficiently optimizing the substituent combinations by iterative rounds of compound sampling, substituent reordering to produce the most regular property landscape, and property estimation over the landscape. Application of this approach to a large pharmaceutical compound library demonstrates its ability to find active compounds with a threefold reduction in synthetic and assaying effort, even without knowing the molecular identity of any compound.