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

## TL;DR

This paper introduces CByG, a Bayesian Flow Network-based framework for controllable 3D molecular generation that optimizes multiple drug-like properties, addressing limitations of previous models in practical drug discovery.

## Contribution

We develop CByG, a gradient-guided conditional generative model extending Bayesian Flow Networks for multi-property control in 3D molecular generation.

## Key findings

- CByG outperforms baseline models in binding affinity, synthetic feasibility, and selectivity.
- The framework demonstrates robustness and practicality for real-world drug discovery.
- A comprehensive evaluation scheme improves assessment of generative models in SBDD.

## 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.