Chemistry-driven Hit-to-lead Optimization Guided by Structure-based Approaches.

ABSTRACT

For several decades, hit identification for drug discovery has been facilitated by developments in both fragment-based and high-throughput screening technologies. However, a major bottleneck in drug discovery projects continues to be the optimization of primary hits from screening campaigns in order to derive lead compounds. Computational chemistry or molecular modeling can play an important role during this hit-to-lead (H2L) stage by both suggesting putative optimizations and decreasing the number of compounds to be experimentally synthesized and evaluated. However, it is also crucial to consider the feasibility of organically synthesizing these virtually designed compounds. Furthermore, the generated molecules should have reasonable physicochemical properties and be medicinally relevant. This review focuses on chemistry-driven and structure-based computational methods that can be used to tackle the difficult problem of H2L optimization, with emphasis being placed on the strategy developed in our laboratory.