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Development of machine learning classifiers to predict compound activity on prostate cancer cell lines
Prostate cancer is the most common type of cancer in men. The disease presents good survival rates if treated at the early stages. However, the evolution of the disease in its most aggressive variant remains without effective therapeutic answers. Therefore, the identification of novel effective ther...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9641853/ https://www.ncbi.nlm.nih.gov/pubmed/36348374 http://dx.doi.org/10.1186/s13321-022-00647-y |
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author | Bonanni, Davide Pinzi, Luca Rastelli, Giulio |
author_facet | Bonanni, Davide Pinzi, Luca Rastelli, Giulio |
author_sort | Bonanni, Davide |
collection | PubMed |
description | Prostate cancer is the most common type of cancer in men. The disease presents good survival rates if treated at the early stages. However, the evolution of the disease in its most aggressive variant remains without effective therapeutic answers. Therefore, the identification of novel effective therapeutics is urgently needed. On these premises, we developed a series of machine learning models, based on compounds with reported highly homogeneous cell-based antiproliferative assay data, able to predict the activity of ligands towards the PC-3 and DU-145 prostate cancer cell lines. The data employed in the development of the computational models was finely-tuned according to a series of thresholds for the classification of active/inactive compounds, to the number of features to be implemented, and by using 10 different machine learning algorithms. Models’ evaluation allowed us to identify the best combination of activity thresholds and ML algorithms for the classification of active compounds, achieving prediction performances with MCC values above 0.60 for PC-3 and DU-145 cells. Moreover, in silico models based on the combination of PC-3 and DU-145 data were also developed, demonstrating excellent precision performances. Finally, an analysis of the activity annotations reported for the ligands in the curated datasets were conducted, suggesting associations between cellular activity and biological targets that might be explored in the future for the design of more effective prostate cancer antiproliferative agents. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-022-00647-y. |
format | Online Article Text |
id | pubmed-9641853 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-96418532022-11-15 Development of machine learning classifiers to predict compound activity on prostate cancer cell lines Bonanni, Davide Pinzi, Luca Rastelli, Giulio J Cheminform Research Prostate cancer is the most common type of cancer in men. The disease presents good survival rates if treated at the early stages. However, the evolution of the disease in its most aggressive variant remains without effective therapeutic answers. Therefore, the identification of novel effective therapeutics is urgently needed. On these premises, we developed a series of machine learning models, based on compounds with reported highly homogeneous cell-based antiproliferative assay data, able to predict the activity of ligands towards the PC-3 and DU-145 prostate cancer cell lines. The data employed in the development of the computational models was finely-tuned according to a series of thresholds for the classification of active/inactive compounds, to the number of features to be implemented, and by using 10 different machine learning algorithms. Models’ evaluation allowed us to identify the best combination of activity thresholds and ML algorithms for the classification of active compounds, achieving prediction performances with MCC values above 0.60 for PC-3 and DU-145 cells. Moreover, in silico models based on the combination of PC-3 and DU-145 data were also developed, demonstrating excellent precision performances. Finally, an analysis of the activity annotations reported for the ligands in the curated datasets were conducted, suggesting associations between cellular activity and biological targets that might be explored in the future for the design of more effective prostate cancer antiproliferative agents. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-022-00647-y. Springer International Publishing 2022-11-08 /pmc/articles/PMC9641853/ /pubmed/36348374 http://dx.doi.org/10.1186/s13321-022-00647-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Bonanni, Davide Pinzi, Luca Rastelli, Giulio Development of machine learning classifiers to predict compound activity on prostate cancer cell lines |
title | Development of machine learning classifiers to predict compound activity on prostate cancer cell lines |
title_full | Development of machine learning classifiers to predict compound activity on prostate cancer cell lines |
title_fullStr | Development of machine learning classifiers to predict compound activity on prostate cancer cell lines |
title_full_unstemmed | Development of machine learning classifiers to predict compound activity on prostate cancer cell lines |
title_short | Development of machine learning classifiers to predict compound activity on prostate cancer cell lines |
title_sort | development of machine learning classifiers to predict compound activity on prostate cancer cell lines |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9641853/ https://www.ncbi.nlm.nih.gov/pubmed/36348374 http://dx.doi.org/10.1186/s13321-022-00647-y |
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