MOLFRA: Online Alignment of Multimodal Molecular Flows via Forward-Process Reinforcement Learning

Pretrained generative models for structure-based drug design (SBDD) learn structural regularities from protein–ligand complexes without directly optimizing task-specific molecular properties. Controllable approaches pursue these objectives through sampling-time guidance or iterative optimization, but improving properties while retaining molecular diversity remains a challenge. We introduce **MOLFRA**, an online forward-process reward-alignment method that jointly fine-tunes pretrained multimodal molecular flows. Under a shared pocket-relative reward, MOLFRA couples implicit positive and negative prediction branches in coordinate endpoint and atom-type logit spaces, entirely avoiding sampling-trajectory likelihood ratios or backpropagation through the sampler. On CrossDocked2020, post-training MOLFORM with a joint Vina Score and synthetic accessibility (SA) reward significantly improves mean Vina Dock (from −7.50 to −9.25 kcal/mol) and normalized SA (from 0.60 to 0.74), while fully preserving the pretrained baseline’s molecular diversity (0.78 vs. 0.78).