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.

基于构象的药物设计中，我们提出了一种使用等变扩散模型 PILOT 的基于计算的方法，通过结合口袋调节和大规模预训练与性质引导，来进行 3D 配体结构的 $	extit {de novo}$ 生成。PILOT 的多目标基于轨迹的重要性抽样策略旨在引导模型生成既具有增强与给定蛋白质口袋结合亲和力等所需特性，同时又保持高合成可访问性的分子。PILOT 在常见基准数据集 CrossDocked2020 上在各种评估指标上明显优于现有方法。此外，我们使用 PILOT 为 Kinodata-3D 数据集中未见蛋白质口袋生成了新的配体结构，该数据集包含了人类蛋白激酶组的大部分内容。生成的结构体预测的 $IC_{50}$ 值表明具有强大的生物活性，突显了 PILOT 作为基于构象的药物设计强大工具的潜力。