Computational methods support nowadays each stage of drug design campaigns. They assist not only in the process of identification of new active compounds towards particular biological target, but also help in the evaluation and optimization of their physicochemical and pharmacokinetic properties. Such features are not less important in terms of the possible turn of a compound into a future drug than its desired affinity profile towards considered proteins. In the study, we focus on metabolic stability, which determines the time that the compound can act in the organism and play its role as a drug. Due to great complexity of xenobiotic transformation pathways in the living organisms, evaluation and optimization of metabolic stability remains a big challenge. Here, we present a novel methodology for the evaluation and analysis of structural features influencing metabolic stability. To this end, we use a well-established explainability method called SHAP. We built several predictive models and analyse their predictions with the SHAP values to reveal how particular compound substructures influence the model’s prediction. The method can be widely applied by users thanks to the web service, which accompanies the article. It allows a detailed analysis of SHAP values obtained for compounds from the ChEMBL database, as well as their determination and analysis for any compound submitted by a user. Moreover, the service enables manual analysis of the possible structural modifications via the provision of analogous analysis for the most similar compound from the ChEMBL dataset. To our knowledge, this is the first attempt to employ SHAP to reveal which substructural features are utilized by machine learning models when evaluating compound metabolic stability. The accompanying web service for metabolic stability evaluation can be of great help for medicinal chemists. Its significant usefulness is related not only to the possibility of assessing compound stability, but also to the provision of information about substructures influencing this parameter. It can assist in the design of new ligands with improved metabolic stability, helping in the detection of privileged and unfavourable chemical moieties during stability optimization. The tool is available at https://metstab-shap.matinf.uj.edu.pl/ .

中文翻译：

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SHAP 值如何帮助塑造化合物的代谢稳定性？

如今，计算方法支持药物设计活动的每个阶段。它们不仅有助于识别针对特定生物靶标的新活性化合物，还有助于评估和优化其理化和药代动力学特性。就化合物可能转化为未来药物而言，这些特征并不比其对所考虑的蛋白质所需的亲和力更重要。在研究中，我们关注代谢稳定性，代谢稳定性决定了化合物在生物体中发挥作用并发挥药物作用的时间。由于生物体内异生物质转化途径非常复杂，代谢稳定性的评估和优化仍然是一个巨大的挑战。这里，我们提出了一种评估和分析影响代谢稳定性的结构特征的新方法。为此，我们使用了一种完善的可解释性方法，称为 SHAP。我们建立了几个预测模型并使用 SHAP 值分析它们的预测，以揭示特定化合物子结构如何影响模型的预测。由于本文附带的网络服务，该方法可以被用户广泛应用。它允许对从 ChEMBL 数据库中获得的化合物的 SHAP 值进行详细分析，以及对用户提交的任何化合物的测定和分析。此外，该服务通过为 ChEMBL 数据集中最相似的化合物提供类似分析，可以对可能的结构修改进行手动分析。据我们所知，这是首次尝试使用 SHAP 来揭示机器学习模型在评估化合物代谢稳定性时利用了哪些子结构特征。随附的代谢稳定性评估网络服务对药物化学家有很大帮助。它的重要用途不仅与评估化合物稳定性的可能性有关，而且与提供有关影响该参数的子结构的信息有关。它可以帮助设计具有更高代谢稳定性的新配体，有助于在稳定性优化过程中检测优先和不利的化学部分。该工具可在 https://metstab-shap.matinf.uj.edu.pl/ 获得。随附的代谢稳定性评估网络服务对药物化学家有很大帮助。它的重要用途不仅与评估化合物稳定性的可能性有关，而且与提供有关影响该参数的子结构的信息有关。它可以帮助设计具有更高代谢稳定性的新配体，有助于在稳定性优化过程中检测优先和不利的化学部分。该工具可在 https://metstab-shap.matinf.uj.edu.pl/ 获得。随附的代谢稳定性评估网络服务对药物化学家有很大帮助。它的重要用途不仅与评估化合物稳定性的可能性有关，而且与提供有关影响该参数的子结构的信息有关。它可以帮助设计具有更高代谢稳定性的新配体，有助于在稳定性优化过程中检测优先和不利的化学部分。该工具可在 https://metstab-shap.matinf.uj.edu.pl/ 获得。它可以帮助设计具有更高代谢稳定性的新配体，有助于在稳定性优化过程中检测优先和不利的化学部分。该工具可在 https://metstab-shap.matinf.uj.edu.pl/ 获得。它可以帮助设计具有更高代谢稳定性的新配体，有助于在稳定性优化过程中检测优先和不利的化学部分。该工具可在 https://metstab-shap.matinf.uj.edu.pl/ 获得。