Wojtuch Agnieszka, Jankowski Rafał, Podlewska Sabina
Faculty of Mathematics and Computer Science, Jagiellonian University, 6 S. Łojasiewicza Street, 30-348, Kraków, Poland.
Maj Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343, Kraków, Poland.
J Cheminform. 2021 Sep 27;13(1):74. doi: 10.1186/s13321-021-00542-y.
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/ .
如今，计算方法支撑着药物设计活动的各个阶段。它们不仅有助于识别针对特定生物靶点的新活性化合物，还能辅助评估和优化其物理化学及药代动力学性质。就化合物可能转化为未来药物而言，这些特性与它对所考虑蛋白质的预期亲和力特征同样重要。在本研究中，我们聚焦于代谢稳定性，它决定了化合物在生物体内发挥作用并作为药物起效的时间。由于生物体中外源物质转化途径极为复杂，代谢稳定性的评估和优化仍然是一项巨大挑战。
在此，我们提出一种用于评估和分析影响代谢稳定性的结构特征的新方法。为此，我们使用一种成熟的可解释性方法，即SHAP。我们构建了多个预测模型，并利用SHAP值分析它们的预测结果，以揭示特定化合物子结构如何影响模型预测。借助本文附带的网络服务，该方法可供用户广泛应用。它允许对从ChEMBL数据库中获取的化合物的SHAP值进行详细分析，也能对用户提交的任何化合物进行SHAP值的测定和分析。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，实现对可能的结构修饰的手动分析。
据我们所知，这是首次尝试利用SHAP来揭示机器学习模型在评估化合物代谢稳定性时所利用的子结构特征。随附的代谢稳定性评估网络服务对药物化学家可能会有很大帮助。它的显著实用性不仅在于能够评估化合物稳定性，还在于提供有关影响该参数的子结构的信息。它可以协助设计具有更高代谢稳定性的新配体，在稳定性优化过程中帮助检测有利和不利的化学基团。该工具可在https://metstab-shap.matinf.uj.edu.pl/获取。
