Generating Multi-Step Chemical Reaction Pathways with Black-Box Optimization

Danny Reidenbach, Connor W. Coley, Kevin Yang

Abstract:

The practical usability of de novo small molecule generation depends heavily on the synthesizability of generated molecules.
We propose BBO-SYN, a generative framework based on black-box optimization (BBO), which predicts diverse molecules with desired properties together with corresponding synthesis pathways. Given an input molecule A, BBO-SYN employs a state-of-the-art BBO method operating on a latent space of molecules to find a reaction partner B, which maximizes the property score of the reaction product C, as determined by a pre-trained template-free reaction predictor. This single-step reaction (A+B→C) forms the basis for an optimization loop, resulting in a synthesis tree yielding products with high property scores. Empirically, the sampling and search strategy of BBO-SYN outperforms comparable baselines on four synthesis-aware optimization tasks (QED, DRD2, GSK3, and JNK3), increasing product diversity by 37% and mean property score by 25% on our hardest JNK3 task.