Division of Applied Regulatory Science, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland 20993, United States.
Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, United States.
ACS Chem Neurosci. 2024 Aug 7;15(15):2842-2852. doi: 10.1021/acschemneuro.4c00212. Epub 2024 Jul 11.
Opioids are small-molecule agonists of μ-opioid receptor (μOR), while reversal agents such as naloxone are antagonists of μOR. Here, we developed machine learning (ML) models to classify the intrinsic activities of ligands at the human μOR based on the SMILES strings and two-dimensional molecular descriptors. We first manually curated a database of 983 small molecules with measured values at the human μOR. Analysis of the chemical space allowed identification of dominant scaffolds and structurally similar agonists and antagonists. Decision tree models and directed message passing neural networks (MPNNs) were then trained to classify agonistic and antagonistic ligands. The hold-out test AUCs (areas under the receiver operator curves) of the extra-tree (ET) and MPNN models are 91.5 ± 3.9% and 91.8 ± 4.4%, respectively. To overcome the challenge of a small data set, a student-teacher learning method called tritraining with disagreement was tested using an unlabeled data set comprised of 15,816 ligands of human, mouse, and rat μOR, κOR, and δOR. We found that the tritraining scheme was able to increase the hold-out AUC of MPNN models to as high as 95.7%. Our work demonstrates the feasibility of developing ML models to accurately predict the intrinsic activities of μOR ligands, even with limited data. We envisage potential applications of these models in evaluating uncharacterized substances for public safety risks and discovering new therapeutic agents to counteract opioid overdoses.
阿片类药物是 μ 型阿片受体（μOR）的小分子激动剂，而纳洛酮等逆转剂是 μOR 的拮抗剂。在这里，我们开发了机器学习（ML）模型，根据 SMILES 字符串和二维分子描述符来分类人 μOR 配体的内在活性。我们首先人工整理了一个数据库，其中包含 983 种在人 μOR 上测量的小分子的值。对化学空间的分析允许确定主要支架以及结构相似的激动剂和拮抗剂。然后，训练决策树模型和有向消息传递神经网络（MPNN）来分类激动剂和拮抗剂配体。外树（ET）和 MPNN 模型的保留测试 AUC（接收器操作曲线下的面积）分别为 91.5±3.9%和 91.8±4.4%。为了克服小数据集的挑战，使用一种名为不一致三重训练的无标签数据集的学生-教师学习方法，该数据集包含 15816 个人、鼠和大鼠 μOR、κOR 和 δOR 的配体。我们发现，三重训练方案能够将 MPNN 模型的保留 AUC 提高到高达 95.7%。我们的工作证明了即使在数据有限的情况下，开发能够准确预测 μOR 配体内在活性的 ML 模型是可行的。我们设想这些模型在评估公共安全风险的未表征物质和发现对抗阿片类药物过量的新治疗剂方面具有潜在应用。
