Chronic pain is a significant health issue that affects approximately 20% of Americans, yet existing treatment options often leave much to be desired. The reliance on opioids for managing pain presents serious challenges, including severe side effects and the risk of dependency. However, a groundbreaking approach is emerging, combining artificial intelligence (AI) with drug discovery to develop more effective and safer pain management solutions.

Innovating Pain Management with AI

Dr. Feixiong Cheng, Director of the Cleveland Clinic’s Genome Center, along with collaborators at IBM, is leveraging AI for drug discovery in the realm of advanced pain management. The research team has developed a deep-learning framework capable of identifying various metabolites derived from the gut microbiome, as well as FDA-approved medications that could be repurposed to offer non-addictive, non-opioid alternatives for chronic pain relief. Their findings, published in Cell Press, illustrate how the Discovery Accelerator partnership is propelling advancements in healthcare and life sciences.

Dr. Yunguang Qiu, a postdoctoral fellow in Dr. Cheng’s lab and co-first author of the study, emphasizes the ongoing challenges of treating chronic pain with opioids. “Drugging a specific subset of pain receptors in a class of proteins called G protein-coupled receptors (GPCRs) can provide non-addictive, non-opioid pain relief,” Dr. Qiu states. The pressing question remains: how can we effectively target those receptors?

A New Approach to Drug Discovery

Rather than inventing entirely new molecules, the research team explored the possibility of utilizing existing methods to identify pre-approved FDA drugs suitable for pain management. This innovative approach involves mapping out gut metabolites to locate viable drug targets.

To facilitate this identification process, the team, led by Dr. Yuxin Yang, a former graduate student from Kent State University, updated an AI-based drug discovery algorithm developed by the Cheng Lab. With support from IBM, the team refined their approach, gaining insights that enhanced their computational techniques. “Our IBM collaborators offered invaluable advice and perspectives,” Dr. Yang remarked. “I’m grateful for the opportunity to collaborate and learn from industry professionals.”

Understanding Molecule Interactions

To determine whether a molecule can serve as a drug, researchers must predict how it will interact with and influence proteins in the body, particularly pain receptors. Achieving this requires a three-dimensional understanding of both the drug and the receptor, based on extensive two-dimensional data regarding their physical, structural, and chemical properties.

Dr. Cheng explains, “Even with current computational methods, combining the amount of data necessary for our predictive analyses is extremely complex and time-consuming. AI can rapidly utilize both compound and protein data gained from imaging, chemical experiments, and evolutionary studies to predict which compound is most likely to influence our pain receptors effectively.”

The LISA-CPI Tool

The research team developed a tool known as LISA-CPI (Ligand Image- and Receptor’s 3D Structures-Aware framework to predict Compound-Protein Interactions). This tool employs deep learning to predict:

• Whether a molecule can bind to a specific pain receptor.
• The specific location on the receptor where a molecule will attach.
• The strength of the attachment between the molecule and the receptor.
• Whether binding a molecule to a receptor will activate or inhibit signaling pathways.

Using LISA-CPI, the team analyzed 369 gut microbial metabolites and 2,308 FDA-approved drugs for their interactions with 13 pain-associated receptors. This AI framework identified several compounds that could be repurposed for effective pain treatment, with laboratory studies currently underway to validate these findings.

Dr. Yang states, “The predictions generated by this algorithm can significantly reduce the experimental burden researchers face when compiling a list of candidate drugs for further testing. We can utilize this tool to evaluate an even broader range of drugs, metabolites, GPCRs, and other receptors to discover therapeutics for diseases beyond pain, such as Alzheimer’s disease.”

The integration of AI in drug discovery represents a significant leap forward in addressing chronic pain management. By identifying potential non-addictive, non-opioid alternatives, researchers are not only working to improve treatment options but also paving the way for safer and more effective healthcare solutions. As this research progresses, the potential for AI to revolutionize drug discovery continues to expand, promising a brighter future for those suffering from chronic pain.