Citations of QsarDB


Citations to the repository models and data

  • Llinas, A.; Avdeef, A. Solubility Challenge Revisited after Ten Years, with Multilab Shake-Flask Data, Using Tight (SD ∼ 0.17 log) and Loose (SD ∼ 0.62 log) Test Sets. J. Chem. Inf. Model., Article ASAP. DOI: 10.1021/acs.jcim.9b00345
    • Used QSAR model is cited in supplementary information
  • Kim, S.; Cho, K. PyQSAR: A Fast QSAR Modeling Platform Using Machine Learning and Jupyter Notebook. Bull. Korean Chem. Soc. 2019, 40, 39-44. DOI: 10.1002/bkcs.11638
    • Used QSAR models are not cited
  • Gómez-Jiménez, G.; Gonzalez-Ponce, K.; Castillo-Pazos, D. J.; Madariaga-Mazon, A.; Barroso-Flores, J.; Cortes-Guzman, F.; Martinez-Mayorga, K. The OECD Principles for (Q)SAR Models in the Context of Knowledge Discovery in Databases (KDD), In: Karabencheva-Christova, T.G.; Christov, C.Z. (Eds.), Advances in Protein Chemistry and Structural Biology, Academic Press, 2018, 85-117. DOI: 10.1016/bs.apcsb.2018.04.001
  • Kausar, S.; Falcao, A. O. An automated framework for QSAR model building. J. Cheminf. 2018, 10:1. DOI: 10.1186/s13321-017-0256-5
  • Önlü, S.; Saçan, M.T. Impact of geometry optimization methods on QSAR modelling: A case study for predicting human serum albumin binding affinity. SAR QSAR Environ. Res. 2017, 28:6, 491-509. DOI: 10.1080/1062936X.2017.1343253
    • Used QSAR model is cited in supplementary information
  • Alder, C. M.; Hayler, J. D.; Henderson, R. K.; Redman, A. M.; Shukla, L.; Shuster, L. E.; Sneddon, H. F. Updating and further expanding GSK's solvent sustainability guide. Green Chem. 2016, 18, 3879-3890. DOI: 10.1039/C6GC00611F
    • Used QSAR model is cited in supplementary information
  • Avdeef, A. Suggested Improvements for Measurement of Equilibrium Solubility-pH of Ionizable Drugs. ADMET & DMPK 2015, 3(2), 84-109. DOI: 10.5599/admet.3.2.193
  • Avdeef, A. Solubility Temperature Dependence Predicted from 2D Structure. ADMET & DMPK 2015, 3(4), 298-344. DOI: 10.5599/admet.3.4.259

QsarDB in the literature

  • Gozalbes, R.; Vicente de Julián-Ortiz, J. Applications of Chemoinformatics in Predictive Toxicology for Regulatory Purposes, Especially in the Context of the EU REACH Legislation. 2018, 1(3), 1-24. DOI: 10.4018/ijqspr.2018010101
  • Nolte, T.M.; Pinto-Gil, K.; Hendriks, A.J.; Ragas, A.M.J.; Pastor, M. Quantitative structure{\textendash}activity relationships for primary aerobic biodegradation of organic chemicals in pristine surface waters: starting points for predicting biodegradation under acclimatization. Environ. Sci.: Processes Impacts, 2018, 20, 157-170. DOI: 10.1039/c7em00375g
  • Patel, M.; Chilton, M.L.; Sartini, A.; Gibson, L.; Barber, C.; Covey-Crump, L.; Przybylak, K.R.; Cronin, M.T.D.; Madden, J.C. Assessment and Reproducibility of Quantitative Structure–Activity Relationship Models by the Nonexpert. J. Chem. Inf. Model. 2018 ,58, 673-682. DOI: 10.1021/acs.jcim.7b00523
  • Gramatica, P.; Papa, E.; Sangion A. QSAR modeling of cumulative environmental endpoints for the prioritization of hazardous chemicals. Environ. Sci.: Processes Impacts, 2018, 20, 38–47.DOI: 10.1039/c7em00519a
  • Haase, A.; Klaessig, F. EU US Roadmap Nanoinformatics 2030, EU Nanosafety Cluster, 2018, November 15. DOI: 10.5281/zenodo.1486012
  • Andersson, N.; Arena, M.; Auteri, D.; Barmaz, S.; Grignard, E.; Kienzler, A.; Lepper, P.; Lostia. A. M.; Munn, S.; Parra Morte, J. M.; Pellizzato, F.; Tarazona, J.; Terron, A.; Van der Linden, S. Guidance for the identification of endocrine disruptors in the context of Regulations (EU) No 528/2012 and (EC) No 1107/2009. EFSA Journal, 2018, 16, 5311. DOI: 10.2903/j.efsa.2018.5311
  • Przybylak, K.R.; Madden, J.C.; Covey-Crump, E.; Gibson, L.; Barber, C.; Patel, M.; Cronin, M.T.D. Characterisation of data resources for in silico modelling: benchmark datasets for ADME properties. Expert Opin. Drug Metab. Toxicol. 2018, 14:2, 169-181. DOI: 10.1080/17425255.2017.1316449
  • Willighagen, E.L.; Mayfield, J.W.; Alvarsson, J.; Berg A.; Carlsson, L.; Jeliazkova, N.; Kuhn, S.; Pluskal, T.; Rojas-Chertó, M.; Spjuth, O.; Torrance, G.; Evelo, C.T.; Guha, R.; Steinbeck, C. The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching. J. Cheminf. 2017, 9:33. DOI: 10.1186/s13321-017-0220-4
  • Worth, A.; Aschberger, K.; Asturiol Bofill, D.; Bessems, J.; Gerloff, K.; Graepel, R.; Joossens, E.; Lamon, L.; Palosaari, T.; Richarz, A. Evaluation of the availability and applicability of computational approaches in the safety assessment of nanomaterials. 2017, EUR 28617 EN, Publications Office of the European Union, Luxembourg, ISBN 978-92-79-68708-2, DOI: 10.2760/248139
  • Settivari, R.S.; Rowlands, J.C.; Wilson, D.M.; Arnold, S.M.; Spencer, P.J. Application of Evolving Computational and Biological Platforms for Chemical Safety Assessment. In: Faqi, A.S. (Ed.), A Comprehensive Guide to Toxicology in Nonclinical Drug Development, Academic Press, 2017, 843–873. DOI: 10.1016/B978-0-12-803620-4.00032-3
  • Mangiatordi, G. F.; Alberga, D.; Altomare, C. D.; Carotti, A.; Catto, M.; Cellamare, S.; Gadaleta, D.; Lattanzi, G.; Leonetti, F.; Pisani, L.; Stefanachi, A.; Trisciuzzi, D.; Nicolotti, O. Mind the Gap! A Journey towards Computational Toxicology. Mol. Inf. 2016, 35, 294–308. DOI: 10.1002/minf.201501017
  • O'Hagan, S.; Kell, D. B. Understanding the foundations of the structural similarities between marketed drugs and endogenous human metabolites. Front. Pharmacol. 2015, 6:105. DOI: 10.3389/fphar.2015.00105
  • Oja, M.; Maran, U. Quantitative structure-permeability relationships at various pH values for acidic and basic drugs and drug-like compounds. SAR QSAR Environ. Res. 2015, 26, 701-719. DOI: 10.1080/1062936X.2015.1085896
  • Oja, M.; Maran, U. The Permeability of an Artificial Membrane for Wide Range of pH in Human Gastrointestinal Tract: Experimental Measurements and Quantitative Structure-Activity Relationship. Mol. Inform., 2015, 34, 493-506. DOI: 10.1002/minf.201400147
  • Warr, W. A. Many InChIs and quite some feat. J. Comput. Aided Mol. Des. 2015, 29, 681-694. DOI: 10.1007/s10822-015-9854-3
  • Clark, A. M.; Dole, K.; Coulon-Spektor, A.; McNutt, A.; Grass, G.; Freundlich, J. S.; Reynolds, R. C.; Ekins, S. Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets. J. Chem. Inf. Model. 2015, 55, 1231-1245. DOI: 10.1021/acs.jcim.5b00143
  • Clark, A. M.; Ekins, S. Open Source Bayesian Models. 2. Mining a “Big Dataset” To Create and Validate Models with ChEMBL. J. Chem. Inf. Model. 2015, 55, 1246-1260. DOI: 10.1021/acs.jcim.5b00144
  • Ambure, P.; Aher, R. B.; Roy, K. Recent Advances in the Open Access Cheminformatics Toolkits, Software Tools, Workflow Environments, and Databases. In: Zhang, W. (Ed.), Computer-Aided Drug Discovery. Methods in Pharmacology and Toxicology, Humana Press, 2015, 1-40. DOI: 10.1007/7653_2014_35
  • Hewitt, M.; Ellison, C. M.; Cronin, M. T. D.; Pastor, M.; Steger-Hartmann, T.; Munoz-Muriendas, J.; Pognan, F.; Madden, J. C. Ensuring confidence in predictions: A scheme to assess the scientific validity of in silico models. Adv. Drug Deliv. Rev. 2015, 101-111. DOI: 10.1016/j.addr.2015.03.005
  • Ekins, S.; Clark, A. M.; Swamidass, S. J.; Litterman, N.; Williams, A. J. Bigger data, collaborative tools and the future of predictive drug discovery. J. Comput. Aided Mol. Des. 2014, 28, 997–1008. DOI: 10.1007/s10822-014-9762-y
  • Piir, G.; Sild, S.; Maran, U. Classifying bio-concentration factor with random forest algorithm, influence of the bio-accumulative vs. non-bio-accumulative compound ratio to modelling result, and applicability domain for random forest model. SAR QSAR Environ. Res. 2014, 25, 967-981. DOI: 10.1080/1062936X.2014.969310
    • First publication that used DOI® System identifier. Predictable models available at the repository
  • Aruoja, V.; Moosus, M.; Kahru, A.; Sihtmäe, M.; Maran, U. Measurement of baseline toxicity and QSAR analysis of 50 non-polar and 58 polar narcotic chemicals for the alga Pseudokirchneriella subcapitata. Chemosphere 2014, 96, 23-32. DOI: 10.1016/j.chemosphere.2013.06.088
  • Palczewska, A.; Fu, X.; Trundle, P.; Yang, L.; Neagu, D.; Ridley, M.; Travis, K. Towards model governance in predictive toxicology. Int. J. Inf. Manag. 2013, 33, 567–582. DOI: 10.1016/j.ijinfomgt.2013.02.005
  • Ruusmann, V.; Maran, U. From data point timelines to a well curated data set, data mining of experimental data and chemical structure data from scientific articles, problems and possible solutions. J. Comput. Aided Mol. Des. 2013, 27, 583-603. DOI: 10.1007/s10822-013-9664-4
    • Example of collection HDL
  • Jeliazkova, N. Web tools for predictive toxicology model building. Expert Opin. Drug Metab. Toxicol. 2012, 8, 791-801. DOI: 10.1517/17425255.2012.685158
  • Cronin, M.T.D. In Silico Tools for Toxicity Prediction. In: Wilson, A.G.E. (Ed.), New Horizons in Predictive Toxicology: Current Status and Application, The Royal Society of Chemistry, 2012, 9-25. DOI: 10.1039/9781849733045-00009
  • Spjuth, O.; Willighagen, E. L.; Guha, R.; Eklund, M.; Wikberg, J. E. S. Towards interoperable and reproducible QSAR analyses: Exchange of datasets. J. Cheminf. 2010, 2:5. DOI: 10.1186/1758-2946-2-5