Deep learning-based molecular generation has extensive applications in many fields, particularly drug discovery. However, the majority of current deep generative models are ligand-based and do not consider chemical knowledge in the molecular generation process, often resulting in a relatively low success rate. We herein propose a structure-based molecular generative framework with chemical knowledge explicitly considered (named PocketFlow), which generates novel ligand molecules inside protein binding pockets. In various computational evaluations, PocketFlow showed state-of-the-art performance, with generated molecules being 100% chemically valid and highly drug-like. Ablation experiments prove the critical role of chemical knowledge in ensuring the validity and drug-likeness of the generated molecules. We applied PocketFlow to two new target proteins that are related to epigenetic regulation, HAT1 and YTHDC1, and successfully obtained wet-lab validated bioactive compounds. The binding modes of the active compounds with target proteins are close to those predicted by molecular docking and further confirmed by the X-ray crystal structure. All the results suggest that PocketFlow is a useful deep generative model, capable of generating innovative bioactive molecules from scratch given a protein binding pocket.

基于深度学习的分子生成在许多领域都有广泛的应用，特别是药物发现。然而，目前的深度生成模型大多数是基于配体的，在分子生成过程中没有考虑化学知识，往往导致成功率相对较低。我们在此提出了一种基于结构的分子生成框架，明确考虑了化学知识（称为 PocketFlow），它在蛋白质结合袋内生成新型配体分子。在各种计算评估中，PocketFlow 表现出了最先进的性能，生成的分子具有 100% 化学有效且高度类似药物。消融实验证明了化学知识在确保生成分子的有效性和药物相似性方面的关键作用。我们将PocketFlow应用于两个与表观遗传调控相关的新靶蛋白HAT1和YTHDC1，并成功获得了湿实验室验证的生物活性化合物。活性化合物与靶蛋白的结合模式与分子对接预测的相近，并通过X射线晶体结构进一步证实。所有结果都表明 PocketFlow 是一种有用的深度生成模型，能够在给定蛋白质结合袋的情况下从头开始生成创新的生物活性分子。