DrugBank

DrugBank: a knowledgebase for drugs, drug actions and drug targets. DrugBank is a richly annotated resource that combines detailed drug data with comprehensive drug target and drug action information. Since its first release in 2006, DrugBank has been widely used to facilitate in silico drug target discovery, drug design, drug docking or screening, drug metabolism prediction, drug interaction prediction and general pharmaceutical education. The latest version of DrugBank (release 2.0) has been expanded significantly over the previous release. With ∼4900 drug entries, it now contains 60% more FDA-approved small molecule and biotech drugs including 10% more ‘experimental’ drugs. Significantly, more protein target data has also been added to the database, with the latest version of DrugBank containing three times as many non-redundant protein or drug target sequences as before (1565 versus 524). Each DrugCard entry now contains more than 100 data fields with half of the information being devoted to drug/chemical data and the other half devoted to pharmacological, pharmacogenomic and molecular biological data. A number of new data fields, including food–drug interactions, drug–drug interactions and experimental ADME data have been added in response to numerous user requests. DrugBank has also significantly improved the power and simplicity of its structure query and text query searches. DrugBank is available at http://www.drugbank.ca


References in zbMATH (referenced in 30 articles , 1 standard article )

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  1. Iliadis, Dimitrios; De Baets, Bernard; Waegeman, Willem: Multi-target prediction for dummies using two-branch neural networks (2022)
  2. Janizek, Joseph D.; Sturmfels, Pascal; Lee, Su-In: Explaining explanations: axiomatic feature interactions for deep networks (2021)
  3. Łukasz Szeremeta; Dominik Tomaszuk: Generating molecular entities as structured data (2021) not zbMATH
  4. Reckell, Trevor; Nguyen, Kyle; Phan, Tin; Crook, Sharon; Kostelich, Eric J.; Kuang, Yang: Modeling the synergistic properties of drugs in hormonal treatment for prostate cancer (2021)
  5. Bullock, Joseph; Luccioni, Alexandra; Pham, Katherine Hoffman; Lam, Cynthia Sin Nga; Luengo-Oroz, Miguel: Mapping the landscape of artificial intelligence applications against COVID-19 (2020)
  6. da Silva, Jairo Gomes; de Morais, Rafael Martins; da Silva, Izabel Cristina Rodrigues; Adimy, Mostafa; de Arruda Mancera, Paulo Fernando: A mathematical model for treatment of papillary thyroid cancer using the Allee effect (2020)
  7. Sanchez, Martin Jose Angel; Petre, Ion: Network controllability analysis of three multiple-myeloma patient genetic mutation datasets (2020)
  8. Moise, Nicolae; Friedman, Avner: Rheumatoid arthritis -- a mathematical model (2019)
  9. Munir, Anum; Azam, Shumaila; Fazal, Sahar; Bhatti, A. I.: Evaluation of the whole body physiologically based pharmacokinetic (WB-PBPK) modeling of drugs (2018)
  10. Zhao, Xian; Chen, Lei; Lu, Jing: A similarity-based method for prediction of drug side effects with heterogeneous information (2018)
  11. Yan, Cheng; Wang, Jianxin; Lan, Wei; Wu, Fang-Xiang; Pan, Yi: SDTRLS: predicting drug-target interactions for complex diseases based on chemical substructures (2017)
  12. Liu, Shengyu; Tang, Buzhou; Chen, Qingcai; Wang, Xiaolong: Drug-drug interaction extraction via convolutional neural networks (2016)
  13. Erdem, Esra; Oztok, Umut: Generating explanations for biomedical queries (2015)
  14. Sun, Peng Gang: Co-controllability of drug-disease-gene network (2015)
  15. Podder, Avijit; Jatana, Nidhi; Latha, N.: Human dopamine receptors interaction network (DRIN): a systems biology perspective on topology, stability and functionality of the network (2014)
  16. Ellabaan, M. M.; Handoko, S. D.; Ong, Y. S.; Kwoh, C. K.; Bahnassy, S. A.; Elassawy, F. M.; Man, H. Y.: A tree-structured covalent-Bond-driven molecular memetic algorithm for optimization of ring-deficient molecules (2012)
  17. Sierra Bello, Omar; Gonzalez, Janneth; Capani, Francisco; Barreto, George E.: In silico docking reveals possible riluzole binding sites on Nav1.6 sodium channel: implications for amyotrophic lateral sclerosis therapy (2012)
  18. Knox, Craig; Law, Vivian; Jewison, Timothy; Liu, Philip; Ly, Son; Frolkis, Alex; Pon, Allison; Banco, Kelly; Mak, Christine; Neveu, Vanessa; Djoumbou, Yannick; Eisner, Roman; Guo, Anchi; Wishart, David S.: Drugbank 3.0: a comprehensive resource for ’omics’ research on drugs (2011) ioport
  19. Sugaya, Nobuyoshi; Furuya, Toshio: Dr. PIAS: an integrative system for assessing the druggability of protein-protein interactions (2011) ioport
  20. Bakheet, Tala; Doig, Andrew J.: Properties and identification of antibiotic drug targets (2010) ioport

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