Machine Learning-based Analysis of Electronic Properties as Predictors of Anticholinesterase Activity in Chalcone Derivatives ...
In this study, we investigated the correlation between the electronic properties of anticholinesterase compounds and their biological activity. While the methodology of such correlation is well-established and has been effectively utilized in previous studies, we employed a more sophisticated approa...
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Online Access: | https://dx.doi.org/10.48550/arxiv.2309.07312 https://arxiv.org/abs/2309.07312 |
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ftdatacite:10.48550/arxiv.2309.07312 2023-11-05T03:44:31+01:00 Machine Learning-based Analysis of Electronic Properties as Predictors of Anticholinesterase Activity in Chalcone Derivatives ... Buzelli, Thiago Ipaves, Bruno Almeida, Wanda Pereira Galvao, Douglas Soares Autreto, Pedro Alves da Silva 2023 https://dx.doi.org/10.48550/arxiv.2309.07312 https://arxiv.org/abs/2309.07312 unknown arXiv Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Computational Physics physics.comp-ph Applied Physics physics.app-ph Data Analysis, Statistics and Probability physics.data-an FOS Physical sciences Article article CreativeWork Preprint 2023 ftdatacite https://doi.org/10.48550/arxiv.2309.07312 2023-10-09T10:57:03Z In this study, we investigated the correlation between the electronic properties of anticholinesterase compounds and their biological activity. While the methodology of such correlation is well-established and has been effectively utilized in previous studies, we employed a more sophisticated approach: machine learning. Initially, we focused on a set of $22$ molecules sharing a common chalcone skeleton and categorized them into two groups based on their IC50 indices: active and inactive. Utilizing the open-source software Orca, we conducted calculations to determine the geometries and electronic structures of these molecules. Over a hundred parameters were collected from these calculations, serving as the foundation for the features used in machine learning. These parameters included the Mulliken and Lowdin electronic populations of each atom within the skeleton, molecular orbital energies, and Mayer's free valences. Through our analysis, we developed numerous models and identified several successful ... : to be submitted to Journal of Chemical Information and Modeling ... Article in Journal/Newspaper Orca DataCite Metadata Store (German National Library of Science and Technology) |
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Computational Physics physics.comp-ph Applied Physics physics.app-ph Data Analysis, Statistics and Probability physics.data-an FOS Physical sciences |
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Computational Physics physics.comp-ph Applied Physics physics.app-ph Data Analysis, Statistics and Probability physics.data-an FOS Physical sciences Buzelli, Thiago Ipaves, Bruno Almeida, Wanda Pereira Galvao, Douglas Soares Autreto, Pedro Alves da Silva Machine Learning-based Analysis of Electronic Properties as Predictors of Anticholinesterase Activity in Chalcone Derivatives ... |
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Computational Physics physics.comp-ph Applied Physics physics.app-ph Data Analysis, Statistics and Probability physics.data-an FOS Physical sciences |
description |
In this study, we investigated the correlation between the electronic properties of anticholinesterase compounds and their biological activity. While the methodology of such correlation is well-established and has been effectively utilized in previous studies, we employed a more sophisticated approach: machine learning. Initially, we focused on a set of $22$ molecules sharing a common chalcone skeleton and categorized them into two groups based on their IC50 indices: active and inactive. Utilizing the open-source software Orca, we conducted calculations to determine the geometries and electronic structures of these molecules. Over a hundred parameters were collected from these calculations, serving as the foundation for the features used in machine learning. These parameters included the Mulliken and Lowdin electronic populations of each atom within the skeleton, molecular orbital energies, and Mayer's free valences. Through our analysis, we developed numerous models and identified several successful ... : to be submitted to Journal of Chemical Information and Modeling ... |
format |
Article in Journal/Newspaper |
author |
Buzelli, Thiago Ipaves, Bruno Almeida, Wanda Pereira Galvao, Douglas Soares Autreto, Pedro Alves da Silva |
author_facet |
Buzelli, Thiago Ipaves, Bruno Almeida, Wanda Pereira Galvao, Douglas Soares Autreto, Pedro Alves da Silva |
author_sort |
Buzelli, Thiago |
title |
Machine Learning-based Analysis of Electronic Properties as Predictors of Anticholinesterase Activity in Chalcone Derivatives ... |
title_short |
Machine Learning-based Analysis of Electronic Properties as Predictors of Anticholinesterase Activity in Chalcone Derivatives ... |
title_full |
Machine Learning-based Analysis of Electronic Properties as Predictors of Anticholinesterase Activity in Chalcone Derivatives ... |
title_fullStr |
Machine Learning-based Analysis of Electronic Properties as Predictors of Anticholinesterase Activity in Chalcone Derivatives ... |
title_full_unstemmed |
Machine Learning-based Analysis of Electronic Properties as Predictors of Anticholinesterase Activity in Chalcone Derivatives ... |
title_sort |
machine learning-based analysis of electronic properties as predictors of anticholinesterase activity in chalcone derivatives ... |
publisher |
arXiv |
publishDate |
2023 |
url |
https://dx.doi.org/10.48550/arxiv.2309.07312 https://arxiv.org/abs/2309.07312 |
genre |
Orca |
genre_facet |
Orca |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.48550/arxiv.2309.07312 |
_version_ |
1781704616269840384 |