Scaffold Hopping with Generative Reinforcement Learning

Scaffold hopping-the design of novel scaffolds for existing lead candidates-is a multifaceted and nontrivial task, for medicinal chemists and computational approaches alike. Generative reinforcement learning can iteratively optimize desirable properties of de novo designs, thereby offering opportunities to accelerate scaffold hopping. Current approaches confine the generation to a predefined molecular substructure (e.g., a linker or scaffold) for scaffold hopping. This confined generation may limit the exploration of the chemical space and require intricate molecule (dis)assembly rules. In this work, we aim to advance reinforcement learning for scaffold hopping, by allowing "unconstrained", full-molecule generation. This is achieved via the RuSH (Reinforcement Learning for Unconstrained Scaffold Hopping) approach. RuSH steers the generation toward the design of full molecules having a high three-dimensional and pharmacophore similarity to a reference molecule, but low scaffold similarity. In this first study, we show the flexibility and effectiveness of RuSH in exploring analogs of known scaffold-hops and in designing scaffold-hopping candidates that match known binding mechanisms. Finally, the comparison between RuSH and two established methods highlights the benefit of its unconstrained molecule generation to systematically achieve scaffold diversity while preserving optimal three-dimensional properties.