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A community computational challenge to predict the activity of pairs of compounds
Open accessAbstract
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.
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“Initiatives like the DREAM Challenges,284 MoleculeNet,110 and Therapeutic Data Commons285 offer benchmark data sets across a wide spectrum of prediction tasks for pharmacology.”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [284]
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Metadata
- Authors
- Community, N.-D., Bansal, M., Yang, J., Karan, C., Menden, M. P., Costello, J. C., Tang, H., Xiao, G., Li, Y., Allen, J., Zhong, R., Chen, B., Kim, M., Wang, T., Heiser, L. M., Realubit, R., Mattioli, M., Alvarez, M. J., Shen, Y., Gallahan, D., Singer, D., Saez-Rodriguez, J., Xie, Y., Stolovitzky, G., & Califano, A.
- Affiliations
- Center for Genomic Science (IT)
- Columbia University (US)
- European Bioinformatics Institute (GB)
- Boston University (US)
- The University of Texas Southwestern Medical Center (US)
- Harold C. Simmons Comprehensive Cancer Center (US)
- Oregon Health & Science University (US)
- Italian Institute of Technology (IT)
- National Institutes of Health (US)
- IBM (United States) (US)
- Herbert Irving Comprehensive Cancer Center (US)
- Corresponding author
- Xie, Yang
- Stolovitzky, Gustavo
- Califano, Andrea
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2014-11-17
- Volume
- 32
- Issue
- 12
- Pages
- 1213-1222
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC4399794
- OpenAlex
- W2036291018
- Cited by
- 321
- References
- 48
- Citations by year
- 2026: 11 · 2025: 25 · 2024: 26 · 2023: 35 · 2022: 30 · 2021: 37 · 2020: 23 · 2019: 31 · 2018: 28 · 2017: 26 · 2016: 28 · 2015: 21
- FWCI
- 25.80
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- public-domain
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion, public-domain)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- İYTE (İZMİR YÜKSEK TEKNOLOJİ ENSTİTÜSÜ) (submittedVersion, public-domain)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- IRIS (submittedVersion)
- IYTE GCRIS Database (Izmir Institute of Technology) (submittedVersion, other-oa)
- SERVAL (Université de Lausanne) (submittedVersion)
- Padua Research Archive (University of Padova) (submittedVersion)
- PMC
- Funders
- Howard Hughes Medical Institute
- Cancer Prevention and Research Institute of Texas (RP101251)
- Wellcome Trust (104104)
- National Institutes of Health (RP101251)
- National Heart, Lung, and Blood Institute (U01HL111566)
- National Human Genome Research Institute (T32HG003284)
- National Cancer Institute (U54CA121852, CA-112970, U01CA164184, U01CA168426, R01CA152301, P50CA058207, P30CA016672, U54CA112970)
- National Institute of General Medical Sciences (R01GM071966, R01GM081871)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- combination therapy, antagonism, in silico prediction, DREAM challenge, community problems, gene expression profiles, human B cells, dose-response curves, syngens
- MeSH terms
- Algorithms, B-Lymphocytes, Computer Simulation, Drug Combinations, Drug Synergism, Humans
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