AI-driven discovery of synergistic drug combinations against pancreatic cancer
Mohsen Pourmousa, Sankalp Jain, Elena Barnaeva, Wengong Jin, Joshua E. Hochuli, Zina Itkin, Travis Maxfield, Cleber C. Melo‐Filho, Andrew Thieme, Kelli M. Wilson, Carleen Klumpp‐Thomas, Sam Michael, Noel Southall, Tommi Jaakkola, Eugene Muratov, Regina Barzilay, Alexander Tropsha, Marc Ferrer, Alexey Zakharov
Abstract
Pancreatic cancer treatment often relies on multi-drug regimens, but optimal combinations remain elusive. This study evaluates predictive approaches to identify synergistic drug combinations using a dataset from the National Center for Advancing Translational Sciences (NCATS). Screening 496 combinations of 32 anticancer compounds against the PANC-1 cells experimentally determined the degree of synergism and antagonism. Three research groups (NCATS, University of North Carolina, and Massachusetts Institute of Technology) leverage these data to apply machine learning (ML) approaches, predicting synergy across 1.6 million combinations. Of the 88 tested, 51 show synergy, with graph convolutional networks achieving the best hit rate and random forest the highest precision. Beyond highlighting the potential of ML, this work delivers 307 experimentally validated synergistic combinations, demonstrating its practical impact in treating pancreatic cancer.