Controllable Generation of Drug-like Molecules with Multi-modal Variational Flow

Fang Sun, Zhihao Zhan, Hongyu Guo, Ming Zhang, Jian Tang, Yizhou Sun

Keywords: Drug Discovery; 3D Geometric Deep Learning; Multimodal Model

Abstract: Designing drug molecules that bind effectively to target proteins while maintaining desired pharmacological properties remains a fundamental challenge in drug discovery. Current approaches struggle to simultaneously control molecular topology and 3D geometry, often requiring expensive retraining for new design objectives. We propose a multi-modal variational flow framework that addresses these limitations by integrating a 2D topology encoder with a 3D geometry generator. Our architecture encodes molecular graphs into a learned latent distribution via junction tree representations, then employs normalizing flows to autoregressively generate atoms in 3D space conditioned on the protein binding site. This design enables zero-shot controllability: by manipulating the latent prior distribution, we can generate molecules with specific substructures or optimized properties without model retraining. Experiments on the CrossDocked benchmark show that our model achieves 31.1% high-affinity rate, substantially outperforming existing methods, while maintaining superior drug-likeness and structural diversity. Our framework opens new possibilities for on-demand molecular design, allowing medicinal chemists to rapidly explore chemical space with precise control over both structural motifs and physicochemical properties.

Submission Type: Full paper proceedings track submission (max 9 main pages).

Submission Number: 19