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AI-driven discovery of synergistic drug combinations against pancreatic cancer
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AI-driven discovery of synergistic drug combinations against pancreatic cancer
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Journal Article
AI-driven discovery of synergistic drug combinations against pancreatic cancer
2025
Overview
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.
Finding optimal multi-drug combinations for pancreatic cancer remains a complex task. Here, authors across three different groups apply machine learning approaches to predict synergy across 1.6 million combinations of drugs for pancreatic cancer, 307 of which are validated experimentally.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
/ Antineoplastic Agents - pharmacology
/ Antineoplastic Combined Chemotherapy Protocols - pharmacology
/ Antineoplastic Combined Chemotherapy Protocols - therapeutic use
/ Drug Screening Assays, Antitumor
/ Humanities and Social Sciences
/ Humans
/ Pancreatic Neoplasms - drug therapy
/ Pancreatic Neoplasms - pathology
/ Science
