Łapińska Natalia, Pacławski Adam, Szlęk Jakub, Mendyk Aleksander
Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, 30-688 Kraków, Poland.
Doctoral School of Medicinal and Health Sciences, Jagiellonian University Medical College, 31-530 Kraków, Poland.
Pharmaceutics. 2024 Mar 1;16(3):349. doi: 10.3390/pharmaceutics16030349.
Understanding the features of compounds that determine their high serotonergic activity and selectivity for specific receptor subtypes represents a pivotal challenge in drug discovery, directly impacting the ability to minimize adverse events while maximizing therapeutic efficacy. Up to now, this process has been a puzzle and limited to a few serotonergic targets. One approach represented in the literature focuses on receptor structure whereas in this study, we followed another strategy by creating AI-based models capable of predicting serotonergic activity and selectivity based on ligands' representation by molecular descriptors. Predictive models were developed using Automated Machine Learning provided by Mljar and later analyzed through the SHAP importance analysis, which allowed us to clarify the relationship between descriptors and the effect on activity and what features determine selective affinity for serotonin receptors. Through the experiments, it was possible to highlight the most important features of ligands based on highly efficient models. These features are discussed in this manuscript. The models are available in the additional modules of the SerotoninAI application called "Serotonergic activity" and "Selectivity".
了解决定化合物高血清素能活性及其对特定受体亚型选择性的特征，是药物研发中的一项关键挑战，直接影响到在将治疗效果最大化的同时将不良事件降至最低的能力。到目前为止，这个过程一直是个谜，并且仅限于少数血清素能靶点。文献中提到的一种方法侧重于受体结构，而在本研究中，我们采用了另一种策略，即创建基于人工智能的模型，该模型能够根据分子描述符对配体的表征来预测血清素能活性和选择性。使用Mljar提供的自动机器学习开发预测模型，随后通过SHAP重要性分析进行分析，这使我们能够阐明描述符与活性影响之间的关系，以及哪些特征决定了对血清素受体的选择性亲和力。通过实验，基于高效模型突出配体的最重要特征成为可能。本文讨论了这些特征。这些模型可在名为“血清素能活性”和“选择性”的SerotoninAI应用程序的附加模块中获得。
