A community computational challenge to predict the activity of pairs of compounds
By Mukesh Bansal, Jichen Yang, Charles Karan, Michael P. Menden, James C. Costello, Hao Tang, Guanghua Xiao, Yajuan Li, Jeffrey D. Allen, R. Zhong and 14 more. 2014
306 citations recorded by the source corpus.
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.
Subfields: Computational Theory and Mathematics, Oncology, Radiology, Nuclear Medicine and Imaging
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In Computational Theory and Mathematics
- Sepp Hochreiter
- Günter Klambauer
- Rajarshi Guha
- Andreas Bender
- Tamás Korcsmáros
- Kristina Preuer
- Richard P. I. Lewis
- Esben Jannik Bjerrum
Citations in the corpus
- Synergy Maps: exploring compound combinations using network-based visualization (2015, methodology): “1) are featured extensively in the literature [11, 15, 16, 21, 23, 25, 29].”
- Combining genomic and network characteristics for extended capability in predicting synergistic drugs for cancer (2015, unknown): “However, drug synergy has emerged as strongly context-dependent, likely reflecting the effects on not only a specific disease but also testing platforms for different cell lines or even drug dosages 8 .”
- Network-based prediction of drug combinations (2019, unknown): “To be sure, several machine learning-based “ black-box ” models have been developed to predict drug combinations 10 – 12 , offering a modest increase in accuracy 10 over random guesses 11 .”
- DeepSynergy: predicting anti-cancer drug synergy with Deep Learning (2017, methodology): “Publicly available databases such as ASDCD (Chen et al. , 2014) containing antifungal combinations and DCDB (Liu et al. , 2010, 2014) with approved and investigational combinations, play a key role in providing good quality training data for developing computational predictive methods.”