title: Biomolecular Network-Based Synergistic Drug Combination Discovery.
abstract: Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease. However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small. To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations. Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system. In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms. Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction. It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing.
authors.affiliation: Key Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), China Ministry of Agriculture, College of Food Science and Technology, Shanghai Ocean University, 999 Hu Cheng Huan Road, Shanghai 201306, China; Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai 201203, China.
authors.forename: Xiangyi
authors.initials: X
authors.lastname: Li
authors.affiliation: Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai 201203, China.
authors.forename: Guangrong
authors.initials: G
authors.lastname: Qin
authors.affiliation: Key Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), China Ministry of Agriculture, College of Food Science and Technology, Shanghai Ocean University, 999 Hu Cheng Huan Road, Shanghai 201306, China; Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai 201203, China.
authors.forename: Qingmin
authors.initials: Q
authors.lastname: Yang
authors.affiliation: Key Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), China Ministry of Agriculture, College of Food Science and Technology, Shanghai Ocean University, 999 Hu Cheng Huan Road, Shanghai 201306, China.
authors.forename: Lanming
authors.identifier: 0000-0001-9537-8476
authors.initials: L
authors.lastname: Chen
authors.affiliation: Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai 201203, China.
authors.forename: Lu
authors.identifier: 0000-0003-0721-2981
authors.initials: L
authors.lastname: Xie
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D004338:Drug Combinations; D055808:Drug Discovery / Q000379:methods*; D004357:Drug Synergism; D006801:Humans; D015398:Signal Transduction / Q000187:drug effects
chemical_list: D004338:Drug Combinations