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Yet one of the major drawbacks of conventional QSAR modelling is that models are set up based on a limited number of experimental and\/or theoretical conditions. To overcome this, the so-called multitasking or multitarget QSAR (mt-QSAR) approaches have emerged as new computational tools able to integrate diverse chemical and biological data into a\n                    <jats:italic>single<\/jats:italic>\n                    model equation, thus extending and improving the reliability of this type of modelling. We have developed\n                    <jats:italic>QSAR-Co-X<\/jats:italic>\n                    , an open source python\u2013based toolkit (available to download at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/ncordeirfcup\/QSAR-Co-X\">https:\/\/github.com\/ncordeirfcup\/QSAR-Co-X<\/jats:ext-link>\n                    ) for supporting mt-QSAR modelling following the Box-Jenkins moving average approach. The new toolkit embodies several functionalities for dataset selection and curation plus computation of descriptors, for setting up linear and non-linear models, as well as for a comprehensive results analysis. The workflow within this toolkit is guided by a cohort of multiple statistical parameters and graphical outputs onwards assessing both the predictivity and the robustness of the derived mt-QSAR models. To monitor and demonstrate the functionalities of the designed toolkit, four case-studies pertaining to previously reported datasets are examined here. We believe that this new toolkit, along with our previously launched\n                    <jats:italic>QSAR-Co<\/jats:italic>\n                    code, will significantly contribute to make mt-QSAR modelling widely and routinely applicable.\n                  <\/jats:p>","DOI":"10.1186\/s13321-021-00508-0","type":"journal-article","created":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T06:18:21Z","timestamp":1618467501000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["QSAR-Co-X: an open source toolkit for multitarget QSAR modelling"],"prefix":"10.1186","volume":"13","author":[{"given":"Amit Kumar","family":"Halder","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3375-8670","authenticated-orcid":false,"given":"M. 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