Predicting the Activities of Drug Excipients on Biological Targets using One-Shot Learning.
Predicting the Activities of Drug Excipients on Biological Targets using One-Shot Learning.
复制标题
使用一次性学习预测药物赋形剂对生物靶标的活性。
DOI：
10.1021/acs.jpcb.1c10574
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Shukla,Diwakar
中科院分区：
文献类型：
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作者：
Mi,Xuenan;Shukla,Diwakar
赋形剂是药物的主要成分，用于改善药物的稳定性和外观等属性。美国食品和药物管理局(FDA)批准的辅料在允许的浓度下对人体是安全的，但它们与药物靶标的潜在相互作用尚未进行系统研究，这可能会影响药物的疗效。深度学习模型已被用于识别可能与药物靶标结合的配体。然而，由于可用数据有限，要可靠地估计配体-蛋白质相互作用的可能性是具有挑战性的。一次学习技术提供了一种潜在的方法来解决这个低数据问题，因为这些技术只需要一个或几个例子来分类新数据。在这项研究中，我们将一次性学习模型应用于包括与G蛋白偶联受体(GPCRs)和激酶结合的配体的数据集。预测结果表明，一次学习可以用于预测配体-蛋白质相互作用，当蛋白质靶标包含保守的结合口袋时，该模型获得了更好的性能。训练好的模型还用于预测辅料与药物靶标之间的相互作用，这为探索药物辅料的活性提供了一种潜在的有效策略。我们发现，大量的药物辅料可以与生物靶点相互作用，影响其功能。结果表明，一次学习可以用来对辅料-蛋白质相互作用做出准确的预测，这些方法可以用于选择药物-蛋白质相互作用有限的辅料。
Excipients are major components of drugs and are used to improve drug attributes such as stability and appearance. Excipients approved by the U.S. Food and Drug Administration (FDA) are regarded as safe for humans in allowed concentrations, but their potential interactions with drug targets have not been investigated systematically, which might influence a drug’s efficacy. Deep learning models have been used for the identification of ligands that could bind to the drug targets. However, due to the limited available data, it is challenging to reliably estimate the likelihood of a ligand–protein interaction. One-shot learning techniques provide a potential approach to address this low data problem as these techniques require only one or a few examples to classify the new data. In this study, we apply one-shot learning models to data sets that include ligands binding to G-protein-coupled receptors (GPCRs) and kinases. The predicted results suggest that one-shot learning could be used for predicting ligand–protein interactions, and the models attain better performance when protein targets contain conserved binding pockets. The trained models are also used to predict interactions between excipients and drug targets, which provides a potential efficient strategy to explore the activities of drug excipients. We find that a large number of drug excipients could interact with biological targets and influence their function. The results demonstrate how one-shot learning can be used to make accurate predictions for excipient–protein interactions, and these methods could be used for selecting excipients with limited drug–protein interactions.