Machine Learned Classification of Ligand Intrinsic Activities at Humanµ-Opioid Receptor

Abstract

AbstractOpioids are small-molecule agonists ofµ-opioid receptor (µOR), while reversal agents such as naloxone are antagonists ofµOR. Here we developed machine learning (ML) models to classify the intrinsic activities of ligands at the humanµOR based on the SMILE strings and two-dimensional molecular descriptors. We first manually curated a database of 983 small molecules with measuredEmaxvalues at the humanµOR. 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 small dataset, a student-teacher learning method called tri-training with disagreement was tested using an unlabeled dataset comprised of 15,816 ligands of human, mouse, or ratµOR,κOR, orδOR. We found that the tri-training scheme was able to increase the hold-out AUC of MPNN to as high as 95.7%. Our work demonstrates the feasibility of developing ML models to accurately predict the intrinsic activities ofµOR 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.TOC Graphic