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A community computational challenge to predict the activity of pairs of compounds

2014; Nature Portfolio; Volume: 32; Issue: 12 Linguagem: Inglês

10.1038/nbt.3052

1546-1696

Mukesh Bansal, Jichen Yang, Charles Karan, Michael P. Menden, James C. Costello, Jing Tang, Guanghua Xiao, Jun Li, Jeffrey D. Allen, Rui Zhong, Beibei Chen, Minsoo Kim, Tao Wang, Laura M. Heiser, Ronald Realubit, Michela Mattioli, Mariano J. Alvarez, Vicky Yao, Daniel Gallahan, Dinah S. Singer, Julio Sáez-Rodríguez, Yang Xie, Gustavo Stolovitzky, Andrea Califano,

CAR-T cell therapy research

A community computational challenge generates algorithms to predict activity of drug combinations. Recent therapeutic successes have renewed interest in drug combinations, but experimental screening approaches are costly and often identify only small numbers of synergistic combinations. The DREAM consortium launched an open challenge to foster the development of in silico methods to computationally rank 91 compound pairs, from the most synergistic to the most antagonistic, based on gene-expression profiles of human B cells treated with individual compounds at multiple time points and concentrations. Using scoring metrics based on experimental dose-response curves, we assessed 32 methods (31 community-generated approaches and SynGen), four of which performed significantly better than random guessing. We highlight similarities between the methods. Although the accuracy of predictions was not optimal, we find that computational prediction of compound-pair activity is possible, and that community challenges can be useful to advance the field of in silico compound-synergy prediction.