Machine Learned Classification of Ligand Intrinsic Activities at Human μ‑Opioid Receptor

• Published in: ACS chemical neuroscience Vol. 15; no. 15; pp. 2842 - 2852
• Main Authors: Oh, Myongin, Shen, Maximilian, Liu, Ruibin, Stavitskaya, Lidiya, Shen, Jana
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 07.08.2024
• Subjects: agonism; SMILES; chemical structure; artificial intelligence; opioid
• ISSN: 1948-7193; 1948-7193
• DOI: 10.1021/acschemneuro.4c00212

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 E max 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.