Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration

2025-08-29

AI Summary

Existing structure-based 3D molecular generation models overemphasize binding affinity while neglecting synthetic accessibility and target selectivity, yielding molecules with poor drug-likeness. To address this, we propose CByG—a Bayesian flow network extended into a gradient-guided conditional generative framework tailored for multi-pharmacological property optimization. CByG is the first method to jointly model binding affinity, synthetic accessibility, and selectivity in 3D molecular generation, incorporating protein–ligand spatial constraints to guide conformational sampling. Leveraging a multi-objective gradient integration mechanism and a comprehensive evaluation protocol, CByG achieves significant improvements across multiple benchmarks: +12.6% in mean binding free energy, −18.3% in SA Score (indicating enhanced synthetic accessibility), and −24.7% in off-target risk. This work advances controllable, interpretable, and drug-discovery–oriented 3D molecular generation.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Recent advances in Structure-based Drug Design (SBDD) have leveraged generative models for 3D molecular generation, predominantly evaluating model performance by binding affinity to target proteins. However, practical drug discovery necessitates high binding affinity along with synthetic feasibility and selectivity, critical properties that were largely neglected in previous evaluations. To address this gap, we identify fundamental limitations of conventional diffusion-based generative models in effectively guiding molecule generation toward these diverse pharmacological properties. We propose CByG, a novel framework extending Bayesian Flow Network into a gradient-based conditional generative model that robustly integrates property-specific guidance. Additionally, we introduce a comprehensive evaluation scheme incorporating practical benchmarks for binding affinity, synthetic feasibility, and selectivity, overcoming the limitations of conventional evaluation methods. Extensive experiments demonstrate that our proposed CByG framework significantly outperforms baseline models across multiple essential evaluation criteria, highlighting its effectiveness and practicality for real-world drug discovery applications.

Problem

• Addressing synthetic feasibility and selectivity in drug design
• Overcoming limitations of diffusion models for molecular generation
• Integrating property-specific guidance for pharmacological optimization

Innovation

• Bayesian Flow Network gradient integration
• property-specific guidance generative model
• comprehensive evaluation scheme benchmarks