Citations of QsarDB

Citations to the repository models and data

  • Biancolillo, A.; Mennitti, L.; Foschi, M.; Marini, F. Advanced Analytical Tools for the Estimation of Gut Permeability of Compounds of Pharmaceutical Interest. Appl. Sci., 2022, 12, 1326. DOI: 10.3390/app12031326
  • Manggara, A. B.; Ohkawa, K.; Sugimoto, M. Classifying Modes of Toxic Action of Molecules with Electronic-Structure Informatics. Application to Imbalanced Toxicity Data of Phenol Derivatives to Tetrahymena Pyriformis. Chem. Lett., 2021. DOI: 10.1246/cl.210453
  • Kovács, D.; Király, P.; Tóth, G. Sample-Size Dependence of Validation Parameters in Linear Regression Models and in QSAR. SAR QSAR Environ. Res., 2021, 1–22. DOI: 10.1080/1062936x.2021.1890208
  • Avdeef, A.; Kans, M. “Flexible-Acceptor” General Solubility Equation for beyond Rule of 5 Drugs. Mol. Pharm., 2020, 17(10), 3930-3940. DOI: 10.1021/acs.molpharmaceut.0c00689
  • Avdeef, A. Prediction of aqueous intrinsic solubility of druglike molecules using Random Forest regression trained with Wiki-pS0 database. ADMET and DMPK, 2020, 8(1), 29-77. 29-77. DOI: 10.5599/admet.766
  • 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., 2019, 59, 3036-3040. 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

  • Biancolillo, A.; Mennitti, L.; Foschi, M.; Marini, F. Advanced Analytical Tools for the Estimation of Gut Permeability of Compounds of Pharmaceutical Interest. Appl. Sci., 2022, 12, 1326. DOI: 10.3390/app12031326
  • Göller, A.H.; Kuhnke, L.; ter Laak, A.; Meier, K.; Hillisch, A. Machine Learning Applied to the Modeling of Pharmacological and ADMET Endpoints. In: Heifetz, A. (Ed.), Artificial Intelligence in Drug Design. Methods in Molecular Biology, vol 2390. Humana, 2022. DOI: 10.1007/978-1-0716-1787-8_2
  • Cronin, M. T. D.; Enoch, S. J.; Madden, J. C.; Rathman, J. F.; Richarz, A.-N.; Yang, C. A Review of in Silico Toxicology Approaches to Support the Safety Assessment of Cosmetics-Related Materials. Comput. Toxicol., 2022, 21, 100213. DOI: 10.1016/j.comtox.2022.100213.
  • Toots, K. M.; Sild, S.; Leis, J.; Acree Jr., W. E.; Maran, U. The Quantitative Structure-Property Relationships for the Gas-Ionic Liquid Partition Coefficient of a Large Variety of Organic Compounds in Three Ionic Liquids. J. Mol. Liq., 2021, 343, 117573. DOI: 10.1016/j.molliq.2021.117573
  • Wang, Y.-L.; Li, J.-Y.; Shi, X.-X.; Wang, Z.; Hao, G.-F.; Yang, G.-F. Web-Based Quantitative Structure–Activity Relationship Resources Facilitate Effective Drug Discovery. Top. Curr. Chem., 2021, 379. DOI: 10.1007/s41061-021-00349-3
  • Manggara, A. B.; Ohkawa, K.; Sugimoto, M. Classifying Modes of Toxic Action of Molecules with Electronic-Structure Informatics. Application to Imbalanced Toxicity Data of Phenol Derivatives to Tetrahymena Pyriformis. Chem. Lett., 2021. DOI: 10.1246/cl.210453
  • García-Sosa, A.T.; Maran, U. Combined Naïve Bayesian, Chemical Fingerprints and Molecular Docking Classifiers to Model and Predict Androgen Receptor Binding Data for Environmentally- and Health-Sensitive Substances. Int. J. Mol. Sci., 2021, 22, 6695. DOI: 10.3390/ijms22136695
  • Kovács, D.; Király, P.; Tóth, G. Sample-Size Dependence of Validation Parameters in Linear Regression Models and in QSAR. SAR QSAR Environ. Res., 2021, 32, 247–268. DOI: 10.1080/1062936x.2021.1890208
  • Jeddi, M.Z.; Hopf, N.B.; Viegas, S.; Price, A.B.; Paini, A.; van Thriel, C.; Benfenati, E.; Ndaw, S.; Bessems, J.; Behnisch, P.A.; Leng, G.; Duca, R.-C.; Verhagen, H.; Cubadda, F.; Brennan, L.; Ali, I.; David, A.; Mustieles, V.; Fernandez, M.F.; Louro, H.; Pasanen-Kase, R. Towards a systematic use of effect biomarkers in population and occupational biomonitoring. Environ. Int., 2021, 146, 106257. DOI: 10.1016/j.envint.2020.106257
  • Piir, G.; Sild, S.; Maran, U. Binary and multi-class classification for androgen receptor agonists, antagonists and binders. Chemosphere, 2021, 262:128313. DOI: 10.1016/j.chemosphere.2020.128313
  • Schaduangrat, N.; Lampa, S.; Simeon, S.; Gleeson, M. P.; Spjuth, O.; Nantasenamat, C. Towards Reproducible Computational Drug Discovery. J. Cheminformatics, 2020, 12. DOI: 10.1186/s13321-020-0408-x
  • Gressling, T. "79 QSAR: quantitative structure–activity relationship". In: Gressling, T., Data Science in Chemistry: Artificial Intelligence, Big Data, Chemometrics and Quantum Computing with Jupyter, Berlin, Boston: De Gruyter, 2020, 386-388. DOI: 10.1515/9783110629453-079
  • Ineris, Rapport annuel 2020, page 44. ISSN 1777-6147.
  • Fayet, G.; Rotureau, P. Chemoinformatics for the Safety of Energetic and Reactive Materials at Ineris. Mol. Inf., 2020, 39, 2000190. DOI: 10.1002/minf.202000190
  • Zukić, S.; Maran, U. Modelling of antiproliferative activity measured in HeLa cervical cancer cells in a series of xanthene derivatives, SAR QSAR Environ. Res., 2020, 31:12, 905-921. DOI: 10.1080/1062936X.2020.1839131
  • Schaduangrat, N.; Lampa, S.; Simeon, S.; Gleeson, M.P.; Spjuth, O.; Nantasenamat, C. Towards reproducible computational drug discovery. J. Cheminform., 2020, 12, 9. DOI: 10.1186/s13321-020-0408-x
  • Gramatica, P. Principles of QSAR Modeling: Comments and Suggestions From Personal Experience. International Journal of Quantitative Structure-Property Relationships (IJQSPR), 2020, 5(3), 61-97. DOI: 10.4018/IJQSPR.20200701.oa1
  • Nantasenamat, C. Best Practices for Constructing Reproducible QSAR Models. In: Roy, K. (Ed.), Ecotoxicological QSARs. Methods in Pharmacology and Toxicology, Humana, 2020, 55-75. DOI: 10.1007/978-1-0716-0150-1_3
  • Nendza, M.; Ahlers, J.; Schwartz, D. Best Practices for Constructing Reproducible QSAR Models. In: Roy, K. (Ed.), Ecotoxicological QSARs. Methods in Pharmacology and Toxicology, Humana, 2020, 545-560. DOI: 10.1007/978-1-0716-0150-1_22
  • Sild, S.; Piir, G.; Neagu, D.; Maran, U. Storing and using quantitative and qualitative structure–activity relationships in the era of toxicological and chemical data expansion. In: Neagu, D.; Richarz, A. (Eds.), Big Data in Predictive Toxicology (series Issues in Toxicology), Royal Society of Chemistry, 2020, 185-213. DOI: 10.1039/9781782623656-00185
  • Käärik, M.; Arulepp, M.; Käärik, M.; Maran, U.; Leis, J. Characterization and prediction of double-layer capacitance of nanoporous carbon materials using the Quantitative nano-Structure-Property Relationship approach based on experimentally determined porosity descriptors. Carbon, 2020, 158, 494-504. DOI: 10.1016/j.carbon.2019.11.017
  • Vighi, M.; Barsi, A.; Focks, A.; Grisoni, F. Predictive models in ecotoxicology: Bridging the gap between scientific progress and regulatory applicability — Remarks and research needs. Integr. Environ. Assess. Manag., 2019, 15, 345-351. DOI: 10.1002/ieam.4136
    • Reference to the QsarDB article is with mistake in first author name
  • Mansouri, K.; Cariello, N.F.; Korotcov, A.; Tkachenko, V.; Grulke, C.M.;Sprankle, Catherine S.; Allen, D.; Casey, W.M.; Kleinstreuer, N.C.; Williams, A.J. Open-source QSAR models for pKa prediction using multiple machine learning approaches. J. Cheminform., 2019, 11, 60. DOI: 10.1186/s13321-019-0384-1
  • Tomonori, I.; Manami, N.; Yuta, H. Revisiting Formic Acid Decomposition by a Graph-Theoretical Approach. J. Phys. Chem. A, 2019, 123 (44), 9579-9586. DOI: 10.1021/acs.jpca.9b05994
  • Hernández-Alvarado, R.B.; Madariaga-Mazón, A.; Martinez-Mayorga, K. Prediction of toxicity of secondary metabolites. Phys. Sci. Rev., 2019, 4(11). DOI: 10.1515/psr-2018-0107
  • García-Sosa, A.T. Benford's law in medicinal chemistry: Implications for drug design. Future Med. Chem., 2019, 11:17, 2247-2253. DOI: 10.4155/fmc-2019-0006
  • Oja, M.; Sild, S.; Maran, U. Logistic Classification Models for pH–Permeability Profile: Predicting Permeability Classes for the Biopharmaceutical Classification System. J. Chem. Inf. Model., 2019, 59(5), 2442-2455. DOI: 10.1021/acs.jcim.8b00833
  • Kazmi, S.R.; Jun, R.; Yu, M.-S.; Jung, C.; Na, D. In silico approaches and tools for the prediction of drug metabolism and fate: A review. Comput. Biol. Med., 2019, 106, 54-64. DOI: 10.1016/j.compbiomed.2019.01.008
  • Richarz, A.-N.; Lamon, L.; Asturiol, D.; Worth, A.P. Current Developments and Recommendations in Computational Nanotoxicology in View of Regulatory Applications. In: Gajewicz, A.; Puzyn, T. (Eds.), Computational Nanotoxicology: Challenges and Perspectives. Jenny Stanford Publishing, 2019, 99-156. DOI: 10.1201/9780429341373
  • Piir, G.; Kahn, I.; García-Sosa, A.T.; Sild, S.; Ahte, P.; Maran, U. Best Practices for QSAR Model Reporting: Physical and Chemical Properties, Ecotoxicity, Environmental Fate, Human Health, and Toxicokinetics Endpoints, Environ. Health Perspect., 2018, 126:12. DOI: 10.1289/EHP3264
  • Oja, M.; Maran, U. pH-permeability profiles for drug substances: Experimental detection, comparison with human intestinal absorption and modelling. Eur. J. Pharm. Sci., 2018, 123, 429-440. DOI: 10.1016/j.ejps.2018.07.014
  • Käärik, M.; Maran, U.; Arulepp, M.; Perkson, A.; Leis, J. Quantitative Nano-Structure–Property Relationships for the Nanoporous Carbon: Predicting the Performance of Energy Storage Materials. ACS Appl. Energy Mater., 2018, 1(8), 4016-4024. DOI: 10.1021/acsaem.8b00708
  • 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–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
  • Puzyn, T.; Jeliazkova, N.; Sarimveis, H.; Marchese Robinson, R.L.; Lobaskin, V.; Rallo, R.; Richarz, A.-N.; Gajewicz, A.; Papadopulos, M.G.; Hastings, J.; Cronin, M.T.D.; Benfenati, E.; Fernández, A. Perspectives from the NanoSafety Modelling Cluster on the validation criteria for (Q)SAR models used in nanotechnology. Food Chem. Toxicol., 2018, 112, 478-494. DOI: 10.1016/j.fct.2017.09.037
    • It should be pointed out that this article includes the following misconception while describing QsarDB: "It is implied that the nanoparticles for which descriptors are calculated can be represented using a CAS number, which is clearly inadequate for describing nanomaterials, as opposed to small molecule chemicals." In reality, QsarDB is not making such statement anywhere in the documentation nor in practical use. The CAS number is a completely optional attribute and QsarDB can use different representations to characterize structures whether small-molecular or in nano-scale. More QnSPR examples can be found in respective collection The article refers to the particular archive, where the use of CAS was relevant (but not mandatory), because this work used monomeric forms and this helped to visualize structures on that level. QsarDB format is open and allows the adaption any suitable structure identification or representation conventions.)
  • Viira, B.; García-Sosa, A.T.; Maran, U. Chemical Structure and Correlation Analysis of HIV-1 NNRT and NRT Inhibitors and Database-Curated, Published Inhibition Constants with Chemical Structure in Diverse Datasets. J. Mol. Graph. Model., 2017, 76, 205–223. DOI: j.jmgm.2017.06.019
  • Tetko, I.V.; Maran, U.; Tropsha, A. Public (Q)SAR Services, Integrated Modeling Environments, and Model Repositories on the Web: State of the Art and Perspectives for Future Development. Mol. Inf., 2016, 36(3), 1600082., DOI: 10.1002/minf.201600082
  • Nolte, T.M.; Ragas, A.M.J. A Review of Quantitative Structure–Property Relationships for the Fate of Ionizable Organic Chemicals in Water Matrices and Identification of Knowledge Gaps. Environ. Sci.: Processes Impacts., 2017, 19 (3), 221–246. DOI: 10.1039/c7em00034k
  • Shoombuatong, W.; Prathipati, P.; Prachayasittikul, V.; Schaduangrat, N.; Malik, A.A.; Pratiwi, R.; Wanwimolruk, S.; S. Wikberg, J.E.; Gleeson, M.P.; Spjuth, O.; Nantasenamat, C. Towards Predicting the Cytochrome P450 Modulation: From QSAR to Proteochemometric Modeling. Curr. Drug Metab., 2017, 18(6), 540-555. DOI: 10.2174/1389200218666170320121932
  • Pham-The, H.; Nam, N.-H.; Nga, D.-V.; Hai, D. T.; Dieguez-Santana, K.; Marrero-Poncee, Y.; Castillo-Garit, J. A.; Casanola-Martin, G. M.; Le-Thi-Thu, H. Learning from Multiple Classifier Systems: Perspectives for Improving Decision Making of QSAR Models in Medicinal Chemistry. Curr. Top. Med. Chem., 2017, 17 (30), 3269–3288. DOI: 10.2174/1568026618666171212111018
  • Ekins, S.; Clark, A.M.; Southan, C.; Bunin, B.A.; Williams, A.J. Small-Molecule Bioactivity Databases. In: Bittker, J.A.; Ross, N.T. (Eds.), High Throughput Screening Methods: Evolution and Refinement, Royal Society of Chemistry, 2017, 344–371. DOI: 10.1039/9781782626770-00344
  • 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
  • Rücker, C.; Mahmoud, W.M.M.; Kümmerer, K. REACH und QSAR: Ein Leitfaden für kleine und mittlere Unternehmen., 2015, Leuphana Universität Lüneburg.
  • Oja, M.; Maran, U. Quantitative Structure–Permeability Relationships at Various PH Values for Neutral and Amphoteric Drugs and Drug-like Compounds. SAR QSAR Environ. Res., 2016, 27 (10), 813–832. DOI: 10.1080/1062936x.2016.1238408
  • 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
  • Mocák, J. Chemometrics in Medicine and Pharmacy. Nova Biotechnologica et Chimica, 2012, 11 (1), 11–26. DOI: 10.2478/v10296-012-0002-3
  • 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