# Fused Bayesian Flow Networks for Dual-Target Molecular Design

arXiv: https://arxiv.org/abs/2608.01007 · HTML: https://mlsanity.com/paper/2608.01007/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 53 references that are in the map.
- First posted on arXiv 2 Aug 2026.

## Builds on (most cited first, 21 of 21)

- [Equivariant Diffusion for Molecule Generation in 3D](https://mlsanity.com/paper/2203.17003/) (2022)
- [GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation](https://mlsanity.com/paper/2203.02923/) (2022)
- [Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities](https://mlsanity.com/paper/2006.02425/) (2020)
- [Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets](https://mlsanity.com/paper/2205.07249/) (2022)
- [3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction](https://mlsanity.com/paper/2303.03543/) (2023)
- [E(n) Equivariant Normalizing Flows](https://mlsanity.com/paper/2105.09016/) (2021)
- [A 3D Generative Model for Structure-Based Drug Design](https://mlsanity.com/paper/2203.10446/) (2022)
- [Generating 3D Molecules for Target Protein Binding](https://mlsanity.com/paper/2204.09410/) (2022)
- [DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design](https://mlsanity.com/paper/2403.07902/) (2024)
- [Multimodal Conditional Image Synthesis with Product-of-Experts GANs](https://mlsanity.com/paper/2112.05130/) (2021)
- [Generating 3D Molecules Conditional on Receptor Binding Sites with Deep Generative Models](https://mlsanity.com/paper/2110.15200/) (2021)
- [Bayesian Flow Networks](https://mlsanity.com/paper/2308.07037/) (2023)
- [MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space](https://mlsanity.com/paper/2404.12141/) (2024)
- [Learning Subpocket Prototypes for Generalizable Structure-based Drug Design](https://mlsanity.com/paper/2305.13997/) (2023)
- [Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization](https://mlsanity.com/paper/2407.01648/) (2024)
- [Reprogramming Pretrained Target-Specific Diffusion Models for Dual-Target Drug Design](https://mlsanity.com/paper/2410.20688/) (2024)
- [Multimodal Variational Autoencoders for Semi-Supervised Learning: In Defense of Product-of-Experts](https://mlsanity.com/paper/2101.07240/) (2021)
- [Empower Structure-Based Molecule Optimization with Gradient Guided Bayesian Flow Networks](https://mlsanity.com/paper/2411.13280/) (2024)
- [Prior-Guided Flow Matching for Target-Aware Molecule Design with Learnable Atom Number](https://mlsanity.com/paper/2509.01486/) (2025)
- [Target-Guided Bayesian Flow Networks for Quantitatively Constrained CAD Generation](https://mlsanity.com/paper/2510.25163/) (2025)
- [Full-Atom Peptide Design via Riemannian-Euclidean Bayesian Flow Networks](https://mlsanity.com/paper/2511.14516/) (2025)

## Abstract (arXiv)

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.