QSAR-Co-X: An Open Source Toolkit for Multi-Target QSAR 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 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 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 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 QSAR-Co code, will significantly contribute to make mt-QSAR modelling widely and routinely applicable.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the latest manuscript can be downloaded and accessed as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
Additional files 1. Folder (CS_1) containing the results (i.e. the output files from the current toolkit) of the FS-LDA, SFS-LDA, RF and GB models for case study 1. Additional files 2. Folder (CS_2) containing both the input files and the results (i.e. the output files from the current toolkit) of the SFS-LDA models for case study 2. Additional files 3. Folder (CS_3) containing input file and the results (i.e. the output files from the current toolkit) obtained from the RF model by applying Method1 for case study 3.
Posted 11 Dec, 2020
On 10 Jan, 2021
Received 07 Jan, 2021
Received 22 Dec, 2020
On 11 Dec, 2020
Invitations sent on 09 Dec, 2020
On 09 Dec, 2020
On 07 Dec, 2020
On 07 Dec, 2020
On 07 Dec, 2020
On 05 Dec, 2020
QSAR-Co-X: An Open Source Toolkit for Multi-Target QSAR Modelling
Posted 11 Dec, 2020
On 10 Jan, 2021
Received 07 Jan, 2021
Received 22 Dec, 2020
On 11 Dec, 2020
Invitations sent on 09 Dec, 2020
On 09 Dec, 2020
On 07 Dec, 2020
On 07 Dec, 2020
On 07 Dec, 2020
On 05 Dec, 2020
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 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 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 QSAR-Co code, will significantly contribute to make mt-QSAR modelling widely and routinely applicable.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the latest manuscript can be downloaded and accessed as a PDF.