ActivityDiff：一种用于 De Novo Drug Design 的具有正向和负向活性引导的扩散模型
ActivityDiff: A diffusion model with Positive and Negative Activity Guidance for De Novo Drug Design
AI总结：
提出基于扩散模型分类器引导的生成方法 ActivityDiff，通过正负向活性引导增强靶点活性并降低脱靶毒性，实现了分子设计中有效性与安全性的平衡控制。
摘要：
实现对分子生物活性的精确控制——包括靶向激活/抑制、协同多靶点调节以及脱靶毒性缓解——仍然是 de novo drug design 中的关键挑战。然而，现有的生成方法主要集中在产生具有单一所需活性的分子，缺乏同时管理多种预期和非预期分子相互作用的集成机制。在此，我们提出了 ActivityDiff，一种基于扩散模型的分类器引导技术的生成方法。它利用分别训练的药物-靶点分类器进行正向和负向引导，使模型能够在增强所需活性的同时，将有害的脱靶效应降至最低。实验结果表明，ActivityDiff 能有效处理关键的药物设计任务，包括单/双靶点生成、片段约束的双靶点设计、增强靶点特异性的选择性生成以及脱靶效应的降低。这些结果证明了分类器引导的扩散在平衡分子设计的有效性和安全性方面的有效性。总体而言，我们的工作引入了一种实现分子活性集成控制的新范式，并将 ActivityDiff 作为一个通用且可扩展的框架提供。
英文摘要：
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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