用扩散模型同时优化药物活性与副作用，实现精准设计。
ActivityDiff: A diffusion model with Positive and Negative Activity Guidance for De Novo Drug Design
- 通过正负靶标分类器引导生成分子，控制目标活性与抑制副作用。
- 在单/双靶点设计、选择性生成和减少脱靶效应上均表现优异。
- 适合需要高特异性与安全性平衡的药物研发人员使用。
在从头药物设计中，精确控制分子的生物活性——包括靶向激活/抑制、多靶点协同调节以及非靶点毒性缓解——仍是重大挑战。现有生成方法主要聚焦于单一活性分子生成，缺乏对多种预期与非预期分子相互作用的集成管理机制。本文提出ActivityDiff，一种基于扩散模型分类器引导技术的生成方法。该方法利用独立训练的药物-靶点分类器进行正向与负向引导，使模型在增强期望活性的同时最小化有害脱靶效应。实验表明，ActivityDiff能有效完成单/双靶点生成、片段约束下的双靶点设计、选择性生成以提升靶点特异性，并降低脱靶风险。结果证明，分类器引导扩散模型可在分子设计中有效平衡疗效与安全性。本工作引入了一种新型集成活性控制范式，提供了可扩展的ActivityDiff框架。
原文摘要 · Abstract (English)
Achieving precise control over a molecule's biological activity-encompassing targeted activation/inhibition, cooperative multi-target modulation, and off-target toxicity mitigation-remains a critical challenge in de novo drug design. However, existing generative methods primarily focus on producing molecules with a single desired activity, lacking integrated mechanisms for the simultaneous management of multiple intended and unintended molecular interactions. Here, we propose ActivityDiff, a generative approach based on the classifier-guidance technique of diffusion models. It leverages separately trained drug-target classifiers for both positive and negative guidance, enabling the model to enhance desired activities while minimizing harmful off-target effects. Experimental results show that ActivityDiff effectively handles essential drug design tasks, including single-/dual-target generation, fragment-constrained dual-target design, selective generation to enhance target specificity, and reduction of off-target effects. These results demonstrate the effectiveness of classifier-guided diffusion in balancing efficacy and safety in molecular design. Overall, our work introduces a novel paradigm for achieving integrated control over molecular activity, and provides ActivityDiff as a versatile and extensible framework.
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