QSAR-Co-X: An Open Source Toolkit for Multi-Target QSAR Modelling

• Type: Other Publications
• Date: 2020

Authors

Natalia N D S Cordeiro (Author) FCUP; Halder, AK (Author) Other

Other information

Authenticus ID: P-00X-J7Y

DOI: 10.21203/rs.3.rs-125264/v1

Abstract (EN): <jats:title>Abstract</jats:title> <jats: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 <jats:italic>single</jats:italic> model equation, thus extending and improving the reliability of this type of modelling. We have developed <jats:italic>QSAR-Co-X</jats:italic>, an open source python¿based 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 <jats:italic>QSAR-Co</jats:italic> code, will significantly contribute to make mt-QSAR modelling widely and routinely applicable.</jats:p>

Language: English

Type (Professor's evaluation): Scientific