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The performance of four machine learning algorithms: Random Forest regression, Stochastic gradient boosting, Multivariate adaptive regression splines and Gaussian processes regression have been studied in order to reach better levels of predictivity. The results for Random Forest and Gaussian processes regression are comparable and outperform other studied methods. The preliminary descriptor selection with Boruta method improved the outcome of machine learning methods. The two novel QSAR\u2010models developed with Random Forest and Gaussian processes regression algorithms have good predictive ability, which was proved by the external evaluation of the test set with corresponding Q<jats:sup>2<\/jats:sup><jats:sub>ext<\/jats:sub>=0.812 and Q<jats:sup>2<\/jats:sup><jats:sub>ext<\/jats:sub>=0.830. The obtained models can be used further for <jats:italic>in silico<\/jats:italic> screening of virtual libraries in the same chemical domain in order to find new antitrypanosomal agents. Thorough analysis of descriptors influence in the QSAR models and interpretation of their chemical meaning allows to highlight a number of structure\u2010activity relationships. The presence of phenyl rings with electron\u2010withdrawing atoms or groups in para\u2010position, increased number of aromatic rings, high branching but short chains, high HOMO energy, and the introduction of 1\u2010substituted 2\u2010indolyl fragment into the molecular structure have been recognized as trypanocidal activity prerequisites.<\/jats:p>","DOI":"10.1002\/minf.201700078","type":"journal-article","created":{"date-parts":[[2017,11,14]],"date-time":"2017-11-14T09:12:59Z","timestamp":1510650779000},"source":"Crossref","is-referenced-by-count":18,"title":["Development of Predictive QSAR Models of 4\u2010Thiazolidinones Antitrypanosomal Activity Using Modern Machine Learning Algorithms"],"prefix":"10.1002","volume":"37","author":[{"given":"Anna","family":"Kryshchyshyn","sequence":"first","affiliation":[{"name":"Department of Pharmaceutical, Organic and Bioorganic Chemistry Danylo Halytsky Lviv National Medical University  Pekarska str. 69 79010 Lviv Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oleg","family":"Devinyak","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Disciplines Uzhgorod National University  Narodna sq. 1 88000 Uzhgorod Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danylo","family":"Kaminskyy","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical, Organic and Bioorganic Chemistry Danylo Halytsky Lviv National Medical University  Pekarska str. 69 79010 Lviv Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philippe","family":"Grellier","sequence":"additional","affiliation":[{"name":"National Museum of Natural History UMR 7245 CNRS MCAM Sorbonne Universit\u00e9s  CP 52, 57 Rue Cuvier Paris 75005 France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3322-0080","authenticated-orcid":false,"given":"Roman","family":"Lesyk","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical, Organic and Bioorganic Chemistry Danylo Halytsky Lviv National Medical University  Pekarska str. 69 79010 Lviv Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2017,11,14]]},"reference":[{"key":"e_1_2_8_2_1","unstructured":"Centers for Disease Control and Prevention: Atlanta GA 2012; https:\/\/www.cdc.gov\/parasites\/sleepingsickness\/ (accessed May 10 2017)."},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(09)60829-1"},{"key":"e_1_2_8_4_1","unstructured":"Trypanosomiasis Human African (Sleeping sickness) Fact sheet; World health Organization: Geneva 2016; http:\/\/www.who.int\/mediacentre\/factsheets\/fs259\/en\/ (accessed May 10 2017)."},{"key":"e_1_2_8_5_1","unstructured":"\u00a0"},{"key":"e_1_2_8_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmech.2004.02.007"},{"key":"e_1_2_8_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmech.2007.06.020"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1128\/AAC.00225-09"},{"key":"e_1_2_8_9_1","unstructured":"\u00a0"},{"key":"e_1_2_8_10_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0056150"},{"key":"e_1_2_8_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmech.2012.07.038"},{"key":"e_1_2_8_12_1","unstructured":"\u00a0"},{"key":"e_1_2_8_13_2","doi-asserted-by":"publisher","DOI":"10.2174\/1568026616666160413125335"},{"key":"e_1_2_8_14_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrmicro.2016.193"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkt1031"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmech.2014.07.092"},{"key":"e_1_2_8_17_1","unstructured":"\u00a0"},{"key":"e_1_2_8_18_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1747-0285.2010.01071.x"},{"key":"e_1_2_8_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bmc.2010.09.056"},{"key":"e_1_2_8_20_2","doi-asserted-by":"publisher","DOI":"10.1002\/cmdc.200700022"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jmedchem.6b00442"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1021\/jm010459j"},{"key":"e_1_2_8_23_1","unstructured":"\u00a0"},{"key":"e_1_2_8_24_2","doi-asserted-by":"publisher","DOI":"10.2174\/1385272043369773"},{"key":"e_1_2_8_25_2","doi-asserted-by":"publisher","DOI":"10.7124\/bc.000089"},{"key":"e_1_2_8_26_1","unstructured":"\u00a0"},{"key":"e_1_2_8_27_2","doi-asserted-by":"publisher","DOI":"10.1517\/17460441.2012.688743"},{"key":"e_1_2_8_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmech.2013.11.017"},{"key":"e_1_2_8_29_2","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/7081_021","volume-title":"QSAR and Molecular Modeling Studies in Heterocyclic Drugs","author":"Prabhakar Y. 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