Faculty, Staff and Student Publications
Publication Date
1-19-2023
Journal
Briefings in Bioinformatics
Abstract
Combination therapy is a promising strategy for confronting the complexity of cancer. However, experimental exploration of the vast space of potential drug combinations is costly and unfeasible. Therefore, computational methods for predicting drug synergy are much needed for narrowing down this space, especially when examining new cellular contexts. Here, we thus introduce CCSynergy, a flexible, context aware and integrative deep-learning framework that we have established to unleash the potential of the Chemical Checker extended drug bioactivity profiles for the purpose of drug synergy prediction. We have shown that CCSynergy enables predictions of superior accuracy, remarkable robustness and improved context generalizability as compared to the state-of-the-art methods in the field. Having established the potential of CCSynergy for generating experimentally validated predictions, we next exhaustively explored the untested drug combination space. This resulted in a compendium of potentially synergistic drug combinations on hundreds of cancer cell lines, which can guide future experimental screens.
Keywords
Drug Synergism, Deep Learning, Computational Biology, Cell Line, Tumor, Antineoplastic Agents, Drug Combinations, drug synergy, deep learning, Chemical Checker, cancer cell lines, untested drug combination space
DOI
10.1093/bib/bbac588
PMID
36562722
PMCID
PMC9851301
PubMedCentral® Posted Date
12-23-2022
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
