Journal
JOURNAL OF CHEMINFORMATICS
Volume 13, Issue 1, Pages -Publisher
BMC
DOI: 10.1186/s13321-021-00508-0
Keywords
QSAR; Multitarget models; Software tools; Feature selection; Machine learning
Categories
Funding
- FCT - Fundacao para a Ciencia e Tecnologia [PTDC/QUI-QIN/30649/2017]
- FCT [UID/QUI/50006/2020]
- Fundação para a Ciência e a Tecnologia [PTDC/QUI-QIN/30649/2017] Funding Source: FCT
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QSAR modelling is a commonly used computational tool, but traditional approaches have limitations due to being based on a limited number of conditions. To overcome this drawback, multitasking or multitarget QSAR (mt-QSAR) methods have emerged to integrate diverse chemical and biological data, enhancing the reliability of modelling.
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 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 single model equation, thus extending and improving the reliability of this type of modelling. We have developed QSAR-Co-X, an open source python-based toolkit (available to download at ) 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 QSAR-Co code, will significantly contribute to make mt-QSAR modelling widely and routinely applicable.
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