Fused Bayesian Flow Networks for Dual-Target Molecular Design

2026-08-02

AI Summary

This work addresses the challenge of generating dual-target molecules in polypharmacology by proposing a distribution-fusion-based 3D molecular generation framework. The approach formulates dual-target binding as a distribution fusion problem within a unified continuous space, dynamically integrating information from both targets via a product-of-experts mechanism and employing a pretrained target-aware Bayesian Flow Network (BFN) as a shared backbone. To mitigate the scarcity of structural data for dual targets, the method introduces chemically aware prior alignment and a prior-free pocket alignment strategy. Experimental results demonstrate that the generated molecules exhibit high binding affinity for both targets while maintaining favorable physicochemical properties, thereby validating the efficacy and novelty of the proposed framework.

Abstract

Dual-target drug design aims to generate 3D molecules that can simultaneously interact with two target proteins, offering a promising route for discovering polypharmacological compounds against complex diseases. While recent generative models have shown encouraging performance in single-target drug design, existing dual-target approaches either focus on sequence generation or introduce an additional predictive drift term into the diffusion-based generative trajectory, which limits their ability to fully integrate feature information from both targets. We propose FusedBFN, a fused Bayesian flow network (BFN) for dual-target molecular design. FusedBFN formulates dual-target generation as distribution fusion in a unified continuous parameter space and employs a product-of-experts formulation to incorporate dual-target information throughout the generative process. To address the scarcity of dual-target structural data, we leverage a pretrained target-aware BFN model as the shared backbone. We further introduce a chemically aware prior-based alignment method and a prior-free pocket alignment strategy to construct aligned dual-target contexts. Extensive experiments demonstrate that FusedBFN generates molecules with strong binding affinity toward dual targets while maintaining favorable molecular properties.

Problem

• dual-target molecular design
• polypharmacology
• 3D molecule generation
• target protein interaction
• molecular binding affinity

Innovation

• Bayesian Flow Networks
• dual-target molecular design
• distribution fusion
• product-of-experts
• pocket alignment