Controllable molecular generation with fine-tuned flow-matching model

Abstract Three-dimensional molecular generative models have emerged that produce de novo molecules both unconditionally and conditionally, e.g., within protein pockets. However, steering those models in a specific region of the chemical space that satisfies a set of desired properties remains challenging. In this study, we introduce a flexible reinforcement learning method for flow-matching based generative models, allowing the velocity field to be refined according to a user-defined reward function. In contrast to a pure conditional generation setup, where the set of conditions must be decided a priori, this framework allows fine-tuning of any unconditional or conditional model, reflecting a more realistic scenario where the target properties to be optimized often vary and are typically case-specific. This also enables joint optimization of continuous and discrete features in flow-matching models for the first time. Through extensive experiments across diverse optimization scenarios, we demonstrate that models trained with this strategy ( agents ) consistently outperform baseline approaches ( priors ) when evaluated against the target design criteria.