PILOT：用于口袋条件从头配体生成的等变扩散模型，通过重要性采样实现多目标引导
PILOT: Equivariant diffusion for pocket conditioned de novo ligand generation with multi-objective guidance via importance sampling
AI总结：
提出等变扩散模型PILOT，结合口袋条件、大规模预训练和多目标重要性采样引导，生成高亲和力且合成可及的配体，在CrossDocked2020上优于现有方法，并为激酶组新口袋生成活性配体。
摘要：
生成既针对给定蛋白质口袋定制又具有一系列所需化学性质的配体，是基于结构的药物设计中的一项重大挑战。在此，我们提出一种使用等变扩散模型PILOT进行三维配体结构从头生成的计算方法，该方法将口袋条件与大规模预训练和性质引导相结合。其基于轨迹的多目标重要性采样策略旨在引导模型生成不仅对给定蛋白质口袋具有增强的结合亲和力等所需特性，而且保持高合成可及性的分子。这确保了采样分子的实用性，从而最大化其在药物发现流程中的潜力。在常见基准数据集CrossDocked2020上，PILOT在各种指标上显著优于现有方法。此外，我们使用PILOT为来自Kinodata-3D数据集的未见蛋白质口袋生成新配体，该数据集涵盖了人类激酶组的很大一部分。生成的结构显示出预测的IC50值，表明具有有效的生物活性，这凸显了PILOT作为基于结构的药物设计强大工具的潜力。
英文摘要：
The generation of ligands that both are tailored to a given protein pocket and exhibit a range of desired chemical properties is a major challenge in structure-based drug design. Here, we propose an in-silico approach for the $\textit{de novo}$ generation of 3D ligand structures using the equivariant diffusion model PILOT, combining pocket conditioning with a large-scale pre-training and property guidance. Its multi-objective trajectory-based importance sampling strategy is designed to direct the model towards molecules that not only exhibit desired characteristics such as increased binding affinity for a given protein pocket but also maintains high synthetic accessibility. This ensures the practicality of sampled molecules, thus maximizing their potential for the drug discovery pipeline. PILOT significantly outperforms existing methods across various metrics on the common benchmark dataset CrossDocked2020. Moreover, we employ PILOT to generate novel ligands for unseen protein pockets from the Kinodata-3D dataset, which encompasses a substantial portion of the human kinome. The generated structures exhibit predicted $IC_{50}$ values indicative of potent biological activity, which highlights the potential of PILOT as a powerful tool for structure-based drug design.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
