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.

翻译：针对特定蛋白质口袋定制且兼具多种理想化学特性的配体生成是基于结构的药物设计中的主要挑战。本文提出一种计算机模拟方法，利用等变扩散模型PILOT实现三维配体结构的从头生成，该方法将口袋条件约束与大规模预训练及属性引导相结合。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具备所需特性（如对给定蛋白质口袋具有更高结合亲和力）且保持高合成可行性的分子。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。在通用基准数据集CrossDocked2020上，PILOT在各项指标上均显著优于现有方法。此外，我们运用PILOT为Kinodata-3D数据集中未见过的蛋白质口袋生成新型配体，该数据集涵盖了人类激酶组的绝大部分。生成结构显示出具有强生物活性的预测$IC_{50}$值，这凸显了PILOT作为基于结构的药物设计强大工具的潜力。