In Silico Target Identification Highlights IL12B as a Candidate for Small Molecule Drug Development

Abstract

Drug discoverys rising costs and complexities require innovative strategies to identify viable therapeutic targets. We developed a computational pipeline to pinpoint protein targets lacking known small molecule probes, focusing on sites traditionally considered challenging for small molecule intervention but validated by FDA-approved biologics. Our approach integrates machine learning, public databases, structural modeling, and functional annotations to prioritize novel binding pockets that overlap biologically validated interfaces. This method identified IL12B as a promising candidate, revealing a previously unexploited surface pocket that overlaps part of the briakinumab epitope. Static protein solvent mapping and dynamic fragment simulations provide convergent evidence of druggability, including fragment-binding clusters and chemically diverse hotspots. While not yet experimentally validated, this site represents a plausible target for orally available IL12B inhibitors. Such compounds could address current clinical limitations of antibody therapies - such as prolonged systemic exposure and infection risk - by enabling a shorter half-life and improved mucosal penetration in diseases like inflammatory bowel disease.