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M. Natália Dias Soeiro Cordeiro
University of Porto
Corresponding Author
ORCiD: https://orcid.org/0000-0003-3375-8670
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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.
Keywords
QSAR, multi-target models, software tools, feature selection, machine learning
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This is a list of supplementary files associated with this preprint. Click to download.
- GraphicalAbstract.jpg
- Supplementaryinformation.zip
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.
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CURRENT STATUS: Published
View the published article at Journal of Cheminformatics.
Version (no preprint)
Posted 20 Feb, 2021
Editorial decision: Major revision
On 20 Feb, 2021
Review #1 received
Received 18 Feb, 2021
Reviewer #1 agreed
On 14 Feb, 2021
Reviewers invited
Invitations sent on 02 Feb, 2021
Editor assigned
On 28 Jan, 2021
Submission checks complete
On 28 Jan, 2021
Editor invited
On 28 Jan, 2021
This version was not preprinted
Version 1
Posted 11 Dec, 2020
No community comments so far
Editorial decision: Major revision
On 10 Jan, 2021
Review #2 received
Received 07 Jan, 2021
Review #1 received
Received 22 Dec, 2020
Reviewer #2 agreed
On 11 Dec, 2020
Reviewers invited
Invitations sent on 09 Dec, 2020
Reviewer #1 agreed
On 09 Dec, 2020
Editor assigned
On 07 Dec, 2020
Submission checks complete
On 07 Dec, 2020
Editor invited
On 07 Dec, 2020
First submitted
On 05 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Artificial Intelligence and Machine Learning
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See the published version of this preprint at Journal of Cheminformatics.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Software
M. Natália Dias Soeiro Cordeiro
University of Porto
Corresponding Author
ORCiD: https://orcid.org/0000-0003-3375-8670
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Journal of Cheminformatics.
Version (no preprint)
Posted 20 Feb, 2021
Editorial decision: Major revision
On 20 Feb, 2021
Review #1 received
Received 18 Feb, 2021
Reviewer #1 agreed
On 14 Feb, 2021
Reviewers invited
Invitations sent on 02 Feb, 2021
Editor assigned
On 28 Jan, 2021
Submission checks complete
On 28 Jan, 2021
Editor invited
On 28 Jan, 2021
This version was not preprinted
Version 1
Posted 11 Dec, 2020
No community comments so far
Editorial decision: Major revision
On 10 Jan, 2021
Review #2 received
Received 07 Jan, 2021
Review #1 received
Received 22 Dec, 2020
Reviewer #2 agreed
On 11 Dec, 2020
Reviewers invited
Invitations sent on 09 Dec, 2020
Reviewer #1 agreed
On 09 Dec, 2020
Editor assigned
On 07 Dec, 2020
Submission checks complete
On 07 Dec, 2020
Editor invited
On 07 Dec, 2020
First submitted
On 05 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Artificial Intelligence and Machine Learning
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Journal of Cheminformatics.
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
This is a list of supplementary files associated with this preprint. Click to download.
- GraphicalAbstract.jpg
- Supplementaryinformation.zip
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.
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