Predictive Models to Identify Small Molecule Activators and Inhibitors of Opioid Receptors

Abstract

Opioid receptors (OPRs) are the main targets for the treatment of pain and related disorders. The opiate compounds that activate these receptors are effective analgesics but their use leads to adverse effects, and they often are highly addictive drugs of abuse. There is an urgent need for alternative chemicals that are analgesics and to reduce/avoid the unwanted effects in order to relieve the public health crisis of opioid addiction. Here, we aim to develop computational models to predict the OPR activity of small molecule compounds based on chemical structures and apply these models to identify novel OPR active compounds. We used four different machine learning algorithms to build models based on quantitative high throughput screening (qHTS) data sets of three OPRs in both agonist and antagonist modes. The best performing models were applied to virtually screen a large collection of compounds. The model predicted active compounds were experimentally validated using the same qHTS assays that generated the training data. Random forest was the best classifier with the highest performance metrics, and the mu OPR (OPRM)-agonist model achieved the best performance measured by AUC-ROC (0.88) and MCC (0.7) values. The model predicted actives resulted in hit rates ranging from 2.3% (delta OPR-agonist) to 15.8% (OPRM-agonist) after experimental confirmation. Compared to the original assay hit rate, all models enriched the hit rate by ≥2-fold. Our approach produced robust OPR prediction models that can be applied to prioritize compounds from large libraries for further experimental validation. The models identified several novel potent compounds as activators/inhibitors of OPRs that were confirmed experimentally. The potent hits were further investigated using molecular docking to find the interactions of the novel ligands in the active site of the corresponding OPR.

Figure 1

The activity class distribution of compounds in the qHTS assay data.

Figure 2

Box plots for the AUC-ROC values showing distributions of 4 machine learning algorithms for different fingerprint types.

Figure 3

Comparison of initial (A) and final (B: Tanimoto score consideration) data analysis of PPV with % active hit rate from the original training set.

Figure 3

Comparison of initial (A) and final (B: Tanimoto score consideration) data analysis of PPV with % active hit rate from the original training set.

Figure 4

Docked poses of the most potent active compounds. The target receptors are shown as grey helices, the active site amino acid residues are represented as lines in grey, the potent compounds shown as sticks with carbons colored in cyan, and the interactions are shown as black dashed lines. Docked poses in top row are for opioid receptor agonists (A-OPRM; B-OPRK; C-OPRD) and the bottom row is for antagonists (D-OPRM; E-OPRK; F-OPRD)