Stereochemistry plays a fundamental role in pharmacology. Here, we systematically investigate the relationship between stereoisomerism and bioactivity on over 1 M compounds, finding that a very significant fraction (~ 40%) of spatial isomer pairs show, to some extent, distinct bioactivities. We then use the 3D representation of these molecules to train a collection of deep neural networks (Signaturizers3D) to generate bioactivity descriptors associated to small molecules, that capture their effects at increasing levels of biological complexity (i.e. from protein targets to clinical outcomes). Further, we assess the ability of the descriptors to distinguish between stereoisomers and to recapitulate their different target binding profiles. Overall, we show how these new stereochemically-aware descriptors provide an even more faithful description of complex small molecule bioactivity properties, capturing key differences in the activity of stereoisomers. Scientific contribution We systematically assess the relationship between stereoisomerism and bioactivity on a large scale, focusing on compound-target binding events, and use our findings to train novel deep learning models to generate stereochemically-aware bioactivity signatures for any compound of interest.

中文翻译：

---

未表征化合物的立体化学感知生物活性描述符


立体化学在药理学中发挥着基础作用。在这里，我们系统地研究了超过 1 M 化合物的立体异构和生物活性之间的关系，发现非常显着的部分（~ 40%）空间异构体对在某种程度上显示出不同的生物活性。然后，我们使用这些分子的 3D 表示来训练一组深度神经网络 (Signaturizers3D)，以生成与小分子相关的生物活性描述符，捕获它们在生物复杂性水平不断增加时的影响（即从蛋白质靶标到临床结果）。此外，我们评估了描述符区分立体异构体和概括其不同靶标结合特征的能力。总的来说，我们展示了这些新的立体化学感知描述符如何提供对复杂小分子生物活性特性的更忠实的描述，捕获立体异构体活性的关键差异。科学贡献我们大规模系统地评估立体异构和生物活性之间的关系，重点关注化合物-靶标结合事件，并利用我们的研究结果训练新颖的深度学习模型，为任何感兴趣的化合物生成立体化学感知的生物活性特征。