We present a user-friendly molecular generative pipeline called Pocket Crafter, specifically designed to facilitate hit finding activity in the drug discovery process. This workflow utilized a three-dimensional (3D) generative modeling method Pocket2Mol, for the de novo design of molecules in spatial perspective for the targeted protein structures, followed by filters for chemical-physical properties and drug-likeness, structure–activity relationship analysis, and clustering to generate top virtual hit scaffolds. In our WDR5 case study, we acquired a focused set of 2029 compounds after a targeted searching within Novartis archived library based on the virtual scaffolds. Subsequently, we experimentally profiled these compounds, resulting in a novel chemical scaffold series that demonstrated activity in biochemical and biophysical assays. Pocket Crafter successfully prototyped an effective end-to-end 3D generative chemistry-based workflow for the exploration of new chemical scaffolds, which represents a promising approach in early drug discovery for hit identification. Hit identification is a time-consuming and costly step in drug discovery process. Here we developed a molecule generative pipeline called Pocket Crafter that can speed up this process greatly. This workflow utilized 3D generative modeling method Pocket2Mol for the de novo design of molecules in spatial perspective for the target and applies filters for chemical-physical properties and drug-likeness to generate top virtual hits with further structure–activity relationship analysis and clustering to output a focused set of hit compounds, which led to the success of hit finding as it showed in our demo case.

中文翻译：

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Pocket Crafter：基于 3D 生成建模的工作流程，用于在药物发现中快速生成命中分子

我们提出了一种名为 Pocket Crafter 的用户友好型分子生成流程，专门用于促进药物发现过程中的命中发现活动。该工作流程利用三维 (3D) 生成建模方法 Pocket2Mol，从头设计目标蛋白质结构的空间视角分子，然后进行化学物理特性和药物相似性、结构活性关系分析的过滤器，并聚类以生成顶级虚拟命中支架。在我们的 WDR5 案例研究中，我们基于虚拟支架在诺华存档库中进行有针对性的搜索后，获得了一组重点关注的 2029 种化合物。随后，我们通过实验对这些化合物进行了分析，产生了一系列新型化学支架，在生物化学和生物物理测定中表现出了活性。 Pocket Crafter 成功构建了一个有效的端到端 3D 生成化学工作流程原型，用于探索新的化学支架，这代表了早期药物发现中用于命中识别的一种有前景的方法。命中识别是药物发现过程中耗时且昂贵的步骤。在这里，我们开发了一种名为 Pocket Crafter 的分子生成管道，可以大大加快这一过程。该工作流程利用 3D 生成建模方法 Pocket2Mol 从头设计目标的空间透视分子，并应用化学​​物理特性和药物相似性过滤器来生成顶级虚拟命中，并进行进一步的结构-活性关系分析和聚类，以输出集中的一组命中化合物，这导致了命中查找的成功，如我们的演示案例所示。