{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"Research Square"}],"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T06:24:40Z","timestamp":1747203880603,"version":"3.40.5"},"posted":{"date-parts":[[2020,12,11]]},"group-title":"In Review","reference-count":0,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2020,12,11]],"date-time":"2020-12-11T00:00:00Z","timestamp":1607644800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2020,12,9]]},"abstract":"<title>Abstract<\/title>\n        <p>Quantitative structure activity relationships (QSAR) modelling is a well-known computational tool, often used in a wide variety of applications. Yet one of the major drawbacks of conventional QSAR modelling tools 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 multi-target QSAR (mt-QSAR) approaches have emerged as new computational tools able to integrate diverse chemical and biological data into a <italic>single<\/italic> model equation, thus extending and improving the reliability of this type of modelling. We have developed <italic>QSAR-Co-X<\/italic>, an open source python\u2212based toolkit (available to download at https:\/\/github.com\/ncordeirfcup\/QSAR-Co-X) 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 along with 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, three case-studies pertaining to previously reported datasets are examined here. We believe that this new toolkit, along with our previously launched <italic>QSAR-Co<\/italic> code, will significantly contribute to make mt-QSAR modelling widely and routinely applicable.<\/p>","DOI":"10.21203\/rs.3.rs-125264\/v1","type":"posted-content","created":{"date-parts":[[2020,12,11]],"date-time":"2020-12-11T20:08:22Z","timestamp":1607717302000},"source":"Crossref","is-referenced-by-count":0,"title":["QSAR-Co-X: An Open Source Toolkit for Multi-Target QSAR Modelling"],"prefix":"10.21203","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3375-8670","authenticated-orcid":false,"given":"M. Nata\u0301lia Dias Soeiro","family":"Cordeiro","sequence":"first","affiliation":[{"name":"University of Porto"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4818-9047","authenticated-orcid":false,"given":"Amit Kumar","family":"Halder","sequence":"additional","affiliation":[{"name":"REQUIMTE LAQV Porto"}]}],"member":"297","container-title":[],"original-title":[],"link":[{"URL":"https:\/\/www.researchsquare.com\/article\/rs-125264\/v1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.researchsquare.com\/article\/rs-125264\/v1.html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,29]],"date-time":"2022-07-29T00:38:28Z","timestamp":1659055108000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.researchsquare.com\/article\/rs-125264\/v1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,11]]},"references-count":0,"URL":"https:\/\/doi.org\/10.21203\/rs.3.rs-125264\/v1","relation":{"is-preprint-of":[{"id-type":"doi","id":"10.1186\/s13321-021-00508-0","asserted-by":"subject"}]},"subject":[],"published":{"date-parts":[[2020,12,11]]},"subtype":"preprint"}}