KUALA: a machine learning-driven framework for kinase inhibitors repositioning
Abstract The family of protein kinases comprises more than 500 genes involved in numerous functions. Hence, their physiological dysfunction has paved the way toward drug discovery for cancer, cardiovascular, and inflammatory diseases. As a matter of fact, Kinase binding sites high similarity has a d...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-22324-8 |
| _version_ | 1797991801366249472 |
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| author | Giada De Simone Davide Stefano Sardina Maria Rita Gulotta Ugo Perricone |
| author_facet | Giada De Simone Davide Stefano Sardina Maria Rita Gulotta Ugo Perricone |
| author_sort | Giada De Simone |
| collection | DOAJ |
| description | Abstract The family of protein kinases comprises more than 500 genes involved in numerous functions. Hence, their physiological dysfunction has paved the way toward drug discovery for cancer, cardiovascular, and inflammatory diseases. As a matter of fact, Kinase binding sites high similarity has a double role. On the one hand it is a critical issue for selectivity, on the other hand, according to poly-pharmacology, a synergistic controlled effect on more than one target could be of great pharmacological interest. Another important aspect of binding similarity is the possibility of exploit it for repositioning of drugs on targets of the same family. In this study, we propose our approach called Kinase drUgs mAchine Learning frAmework (KUALA) to automatically identify kinase active ligands by using specific sets of molecular descriptors and provide a multi-target priority score and a repurposing threshold to suggest the best repurposable and non-repurposable molecules. The comprehensive list of all kinase-ligand pairs and their scores can be found at https://github.com/molinfrimed/multi-kinases . |
| first_indexed | 2024-04-11T08:58:00Z |
| format | Article |
| id | doaj.art-138a37614b4e46a3bbeea2ee7e6c0dc0 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T08:58:00Z |
| publishDate | 2022-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-138a37614b4e46a3bbeea2ee7e6c0dc02022-12-22T04:33:08ZengNature PortfolioScientific Reports2045-23222022-10-0112111610.1038/s41598-022-22324-8KUALA: a machine learning-driven framework for kinase inhibitors repositioningGiada De Simone0Davide Stefano Sardina1Maria Rita Gulotta2Ugo Perricone3Molecular Informatics Group, Fondazione Ri.MEDMolecular Informatics Group, Fondazione Ri.MEDMolecular Informatics Group, Fondazione Ri.MEDMolecular Informatics Group, Fondazione Ri.MEDAbstract The family of protein kinases comprises more than 500 genes involved in numerous functions. Hence, their physiological dysfunction has paved the way toward drug discovery for cancer, cardiovascular, and inflammatory diseases. As a matter of fact, Kinase binding sites high similarity has a double role. On the one hand it is a critical issue for selectivity, on the other hand, according to poly-pharmacology, a synergistic controlled effect on more than one target could be of great pharmacological interest. Another important aspect of binding similarity is the possibility of exploit it for repositioning of drugs on targets of the same family. In this study, we propose our approach called Kinase drUgs mAchine Learning frAmework (KUALA) to automatically identify kinase active ligands by using specific sets of molecular descriptors and provide a multi-target priority score and a repurposing threshold to suggest the best repurposable and non-repurposable molecules. The comprehensive list of all kinase-ligand pairs and their scores can be found at https://github.com/molinfrimed/multi-kinases .https://doi.org/10.1038/s41598-022-22324-8 |
| spellingShingle | Giada De Simone Davide Stefano Sardina Maria Rita Gulotta Ugo Perricone KUALA: a machine learning-driven framework for kinase inhibitors repositioning Scientific Reports |
| title | KUALA: a machine learning-driven framework for kinase inhibitors repositioning |
| title_full | KUALA: a machine learning-driven framework for kinase inhibitors repositioning |
| title_fullStr | KUALA: a machine learning-driven framework for kinase inhibitors repositioning |
| title_full_unstemmed | KUALA: a machine learning-driven framework for kinase inhibitors repositioning |
| title_short | KUALA: a machine learning-driven framework for kinase inhibitors repositioning |
| title_sort | kuala a machine learning driven framework for kinase inhibitors repositioning |
| url | https://doi.org/10.1038/s41598-022-22324-8 |
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