提出CByG框架，实现可控制的3D分子生成，兼顾结合力、可合成性和选择性。
Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration
- 基于贝叶斯流网络与梯度融合，实现多药理属性的可控生成
- 在结合力、可合成性、选择性上均显著优于基线模型
- 适用于真实药物发现场景，评估体系更贴近实际需求
结构基础药物设计（SBDD）近年来采用生成模型进行3D分子生成，主要以目标蛋白结合亲和力评估模型性能。但实际药物发现还需兼顾高结合亲和力、可合成性与选择性，这些关键性质此前常被忽视。我们指出传统扩散生成模型在引导分子生成以满足多样药理属性方面的根本局限。为此，提出CByG框架，将贝叶斯流网络扩展为基于梯度的条件生成模型，实现属性特异性引导的稳健集成。同时，构建包含结合亲和力、可合成性与选择性在内的综合评估体系，克服传统评估方法的不足。大量实验表明，所提CByG框架在多个关键评价指标上显著优于基线模型，凸显其在真实药物发现中的有效性和实用性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。