Machine Learning augmented docking studies of aminothioureas at the SARS-CoV-2-ACE2 interface.
The current pandemic outbreak clearly indicated the urgent need for tools allowing fast predictions of bioactivity of a large number of compounds, either available or at least synthesizable. In the computational chemistry toolbox, several such tools are available, with the main ones being docking an...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0256834 |
| _version_ | 1818892080584327168 |
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| author | Monika Rola Jakub Krassowski Julita Górska Anna Grobelna Wojciech Płonka Agata Paneth Piotr Paneth |
| author_facet | Monika Rola Jakub Krassowski Julita Górska Anna Grobelna Wojciech Płonka Agata Paneth Piotr Paneth |
| author_sort | Monika Rola |
| collection | DOAJ |
| description | The current pandemic outbreak clearly indicated the urgent need for tools allowing fast predictions of bioactivity of a large number of compounds, either available or at least synthesizable. In the computational chemistry toolbox, several such tools are available, with the main ones being docking and structure-activity relationship modeling either by classical linear QSAR or Machine Learning techniques. In this contribution, we focus on the comparison of the results obtained using different docking protocols on the example of the search for bioactivity of compounds containing N-N-C(S)-N scaffold at the S-protein of SARS-CoV-2 virus with ACE2 human receptor interface. Based on over 1800 structures in the training set we have predicted binding properties of the complete set of nearly 600000 structures from the same class using the Machine Learning Random Forest Regressor approach. |
| first_indexed | 2024-12-19T17:51:01Z |
| format | Article |
| id | doaj.art-46b2edb30ec148f6b53976bcf1873f07 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-19T17:51:01Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-46b2edb30ec148f6b53976bcf1873f072022-12-21T20:11:55ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01169e025683410.1371/journal.pone.0256834Machine Learning augmented docking studies of aminothioureas at the SARS-CoV-2-ACE2 interface.Monika RolaJakub KrassowskiJulita GórskaAnna GrobelnaWojciech PłonkaAgata PanethPiotr PanethThe current pandemic outbreak clearly indicated the urgent need for tools allowing fast predictions of bioactivity of a large number of compounds, either available or at least synthesizable. In the computational chemistry toolbox, several such tools are available, with the main ones being docking and structure-activity relationship modeling either by classical linear QSAR or Machine Learning techniques. In this contribution, we focus on the comparison of the results obtained using different docking protocols on the example of the search for bioactivity of compounds containing N-N-C(S)-N scaffold at the S-protein of SARS-CoV-2 virus with ACE2 human receptor interface. Based on over 1800 structures in the training set we have predicted binding properties of the complete set of nearly 600000 structures from the same class using the Machine Learning Random Forest Regressor approach.https://doi.org/10.1371/journal.pone.0256834 |
| spellingShingle | Monika Rola Jakub Krassowski Julita Górska Anna Grobelna Wojciech Płonka Agata Paneth Piotr Paneth Machine Learning augmented docking studies of aminothioureas at the SARS-CoV-2-ACE2 interface. PLoS ONE |
| title | Machine Learning augmented docking studies of aminothioureas at the SARS-CoV-2-ACE2 interface. |
| title_full | Machine Learning augmented docking studies of aminothioureas at the SARS-CoV-2-ACE2 interface. |
| title_fullStr | Machine Learning augmented docking studies of aminothioureas at the SARS-CoV-2-ACE2 interface. |
| title_full_unstemmed | Machine Learning augmented docking studies of aminothioureas at the SARS-CoV-2-ACE2 interface. |
| title_short | Machine Learning augmented docking studies of aminothioureas at the SARS-CoV-2-ACE2 interface. |
| title_sort | machine learning augmented docking studies of aminothioureas at the sars cov 2 ace2 interface |
| url | https://doi.org/10.1371/journal.pone.0256834 |
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