Deep generative modeling has a strong potential to accelerate drug design. However, existing generative models often face challenges in generalization due to limited data, leading to less innovative designs with often unfavorable interactions for unseen target proteins. To address these issues, we propose an interaction-aware 3D molecular generative framework that enables interaction-guided drug design inside target binding pockets. By leveraging universal patterns of protein-ligand interactions as prior knowledge, our model can achieve high generalizability with limited experimental data. Its performance has been comprehensively assessed by analyzing generated ligands for unseen targets in terms of binding pose stability, affinity, geometric patterns, diversity, and novelty. Moreover, the effective design of potential mutant-selective inhibitors demonstrates the applicability of our approach to structure-based drug design.

深度生成模型具有加速药物设计的巨大潜力。然而，由于数据有限，现有的生成模型常常面临泛化方面的挑战，导致设计创新性较差，并且与看不见的目标蛋白之间往往存在不利的相互作用。为了解决这些问题，我们提出了一种相互作用感知的 3D 分子生成框架，该框架能够在目标结合口袋内进行相互作用引导的药物设计。通过利用蛋白质-配体相互作用的通用模式作为先验知识，我们的模型可以利用有限的实验数据实现高度的通用性。通过分析生成的未见靶标配体的结合姿势稳定性、亲和力、几何图案、多样性和新颖性，对其性能进行了全面评估。此外，潜在突变选择性抑制剂的有效设计证明了我们的方法对基于结构的药物设计的适用性。