FusedBFN: Probabilistic Composition for Dual-Target Molecular Generation

Dual-target drug design aims to generate 3D molecules that can be compatible with two distinct protein binding pockets, yet the scarcity of dual-target structural data makes direct model training difficult. We study this problem in a zero-shot setting by employing pretrained single-target generative models and investigate where target-specific information should be composed within the generative process. We propose FusedBFN, which combines two target-aware sender likelihoods using a weighted Product of Experts before performing a shared Bayesian update. To examine composition at different probabilistic levels, we further introduce CompBFN and FusedDiff, which, together with CompDiff, instantiate distribution fusion and transition-kernel composition under both Bayesian flow and diffusion backbones. Our analysis reveals that seemingly different composition mechanisms can become equivalent under matched settings, while their differences otherwise depend on the induced uncertainty and generative dynamics. We also introduce a chemically weighted prior-based alignment method and a prior-free pocket alignment strategy to construct aligned dual-target contexts. Extensive experiments show that FusedBFN achieves strong dual-target generation performance, and further reveal that reducing sampling uncertainty around the fused signal can substantially improve average dual-target binding. These results clarify how the probabilistic formulation of composition interacts with the chosen generative framework in dual-target molecular design. The source code is provided at https://anonymous.4open.science/r/FusedBFN/.