Circumventing the Synthesizability Problem in Generative Molecular Design

Generative structure-based drug design (SBDD) models have shown great promise for accelerating our ability to discover novel drug candidates. However, these models have been criticized for producing compounds that are not very synthesizable and therefore not practically applicable to drug design. In this work, we propose a way to circumvent the synthesizability issue by introducing a model-guided virtual screening (MGVS) pipeline that pairs SBDD models with efficient chemical similarity search methods to identify synthesizable analogues of generated compounds in existing ultralarge compound databases. Using this approach, we demonstrate that synthesizable analogues of generated compounds with equivalent or better docking scores and similar predicted binding poses can be reliably identified across a wide range of protein targets. We find that MGVS outperforms standard virtual ligand screening (VLS), consistently yielding at least a 25x improvement in docking-based screening efficiency across three different SBDD models. As drug-like chemical spaces continue to grow and standard VLS methods focused on exhaustive screening become increasingly impractical, approaches such as MGVS that effectively narrow the search space will become critical for advancing drug discovery.