Molecular Modelling and Drug Design Lab, Institute for Drug Research and Fraunhofer Project Center for Drug Discovery and Delivery, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 91905, Israel.
J Chem Inf Model. 2023 Jun 12;63(11):3248-3262. doi: 10.1021/acs.jcim.2c01531. Epub 2023 May 31.
G protein-coupled receptors (GPCRs) are targets of many drugs, of which ∼25% are indicated for central nervous system (CNS) disorders. Drug promiscuity affects their efficacy and safety profiles. Predicting the polypharmacology profile of compounds against GPCRs can thus provide a basis for producing more precise therapeutics by considering the targets and the anti-targets in that family of closely related proteins. We provide a tool for predicting the polypharmacology of compounds within prominent GPCR families in the CNS: serotonin, dopamine, histamine, muscarinic, opioid, and cannabinoid receptors. Our in-house algorithm, "iterative stochastic elimination" (ISE), produces high-quality ligand-based models for agonism and antagonism at 31 GPCRs. The ISE models correctly predict 68% of CNS drug-GPCR interactions, while the "similarity ensemble approach" predicts only 33%. The activity models correctly predict 56% of reported activities of DrugBank molecules for these CNS receptors. We conclude that the combination of interactions and activity profiles generated by screening through our models form the basis for subsequent designing and discovering novel therapeutics, either single, multitargeting, or repurposed.
G 蛋白偶联受体（GPCRs）是许多药物的靶点，其中约 25%用于治疗中枢神经系统（CNS）疾病。药物混杂性会影响其疗效和安全性特征。因此，预测化合物对 GPCR 的多效性特征可以为通过考虑该家族中密切相关的蛋白质的靶标和抗靶标来生产更精确的治疗药物提供基础。我们提供了一种用于预测 CNS 中主要 GPCR 家族内化合物的多效性的工具：5-羟色胺、多巴胺、组胺、毒蕈碱、阿片样物质和大麻素受体。我们的内部算法“迭代随机消除”（ISE）为 31 种 GPCR 的激动和拮抗作用生成了高质量的基于配体的模型。ISE 模型正确预测了 68%的 CNS 药物-GPCR 相互作用，而“相似性整体方法”仅预测了 33%。这些 CNS 受体的活性模型正确预测了 DrugBank 分子报告的 56%的活性。我们的结论是，通过我们的模型筛选生成的相互作用和活性谱组合为随后的设计和发现新型治疗药物提供了基础，无论是单一、多靶向还是重新定位。
Zotero 插件
