Prioritizing Pain-Associated Targets with Machine Learning

Abstract

While hundreds of genes have been associated with pain, much of the molecular mechanisms of pain remain unknown. As a result, current analgesics are limited to few clinically validated targets. Here, we trained a machine learning (ML) ensemble model to predict new targets for 17 categories of pain. The model utilizes features from transcriptomics, proteomics, and gene ontology to prioritize targets for modulating pain. We focused on identifying novel G-protein-coupled receptors (GPCRs), ion channels, and protein kinases because these proteins represent the most successful drug target families. The performance of the model to predict novel pain targets is 0.839 on average based on AUROC, while the predictions for arthritis had the highest accuracy (AUROC = 0.929). The model predicts hundreds of novel targets for pain; for example, GPR132 and GPR109B are highly ranked GPCRs for rheumatoid arthritis. Overall, gene-pain association predictions cluster into three groups that are enriched for cytokine, calcium, and GABA-related cell signaling pathways. These predictions can serve as a foundation for future experimental exploration to advance the development of safer and more effective analgesics.

Getting Started with the IDG KMC Datasets and Tools

“…Utilizing these collected and annotated databases generates opportunities for machine learning-ready platforms. For example, using these tools (i.e., combining data on genes, proteins, and RNA molecules from fourteen databases and publications), the IDG-KMC developed a machine learning algorithm that prioritizes targets for human genes associated with 17 unique types of pain, and identified thirteen potential IDG family drug targets for migraine drug development and four for rheumatoid arthritis (Jeon, Jagodnik, Kropiwnicki, Stein, & Ma'ayan, 2021). Here we provide a collection of step-by-step get-started protocols to gain initial access to the resources created by the IDG-KMC.…”

Introduction to the Biochemistry of Pain Special Issue

“…In this issue, Jeon et al . utilize advanced machine learning approaches to identify and prioritize novel pain targets.…”

“…In this issue, Jeon et al 6 utilize advanced machine learning approaches to identify and prioritize novel pain targets. They have been able to identify hundreds of potentially new analgesic targets, including, for example, the orphan GPCRs GPR132 and GPR109B.…”