New Delhi, Oct 4 A team of researchers are using artificial intelligence (AI) to discover existing drugs to help patients suffering from chronic pain.
The team from Cleveland Clinic joined hands with tech giant IBM and used their deep-learning framework to find multiple gut microbiome-derived metabolites and FDA-approved drugs that are non-addictive and non-opioid and which be repurposed to treat chronic pain.
Treating chronic pain with opioids is still a challenge due to the risk of severe side effects and dependency, said co-first author Yunguang Qiu, postdoctoral fellow At Cleveland Clinic.
In the study, published in the journal Cell Press, the team focussed on mapping out gut metabolites to spot drug targets.
They used AI to decode both compound and protein data “to predict which compound has the best chance of influencing our pain receptors in the right way”.
This would be extremely complex and time-consuming with current computational methods.
Using their deep-learning model LISA-CPI (Ligand Image- and receptor's three-dimensional (3D) Structures-Aware framework to predict Compound-Protein Interactions), the team predicted how 369 gut microbial metabolites and 2,308 US FDA- approved drugs would interact with 13 pain-associated receptors.
Several compounds that could be repurposed to treat pain were identified using the AI framework. Laboratory studies are currently underway to validate these.
The team noted that using the algorithm to predict drugs’ potential can lower “the experimental burden researchers must overcome to even come up with a list of candidate drugs for further testing”.
Besides drugs for pain management, the tool can also be applied to find drugs and metabolites that treat diseases like Alzheimer’s.
The team noted that these foundation models have the potential to enhance “AI technologies to rapidly develop therapeutics for multiple challenging human health issues”.
Disclaimer: This post has been auto-published from an agency feed without any modifications to the text and has not been reviewed by an editor