Machine learning based QSAR and Molecular Dynamics simulations in the structural design and mechanism of action of imidazole derivatives with anti-melanoma activity
Abstract Cancer is defined as a group of diseases in which abnormal cells multiply and can invade other organs, requiring continuous studies for new drugs. A series of 177 imidazo[1,2-a]pyridine and imidazo[1,2-a] pyrazine synthetic derivatives were previously obtained, and their anti-melanoma IC50...
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| Format: | Article |
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Universidade de São Paulo
2025-01-01
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| Series: | Brazilian Journal of Pharmaceutical Sciences |
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| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1984-82502025000100335&lng=en&tlng=en |
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| _version_ | 1832592514237857792 |
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| author | Rosalvo Ferreira de Oliveira Neto Sérgio Ruschi Bergamachi Silva Cintia Emi Yanaguibashi Leal Edilson Beserra de Alencar Filho |
| author_facet | Rosalvo Ferreira de Oliveira Neto Sérgio Ruschi Bergamachi Silva Cintia Emi Yanaguibashi Leal Edilson Beserra de Alencar Filho |
| author_sort | Rosalvo Ferreira de Oliveira Neto |
| collection | DOAJ |
| description | Abstract Cancer is defined as a group of diseases in which abnormal cells multiply and can invade other organs, requiring continuous studies for new drugs. A series of 177 imidazo[1,2-a]pyridine and imidazo[1,2-a] pyrazine synthetic derivatives were previously obtained, and their anti-melanoma IC50 values have been determined. Here, Artificial Intelligence algorithms were used to select molecular descriptors and build a QSAR model, highlighting structural characteristics related to enhanced molecular potency. Additionally, the imidazopyrazine nucleus was compared to a known inhibitor of the Aurora Kinase enzyme, an important target in cancer therapy. Thus, strategic imidazopyrazines were subjected to comparative molecular dynamics calculations, providing inferences about their possible mechanisms of action. The QSAR model allows for the design and prediction of nine new analogues with favourable predicted IC50 values. Molecular dynamics simulations and the estimated binding energies are consistent with the ranking of activities presented by representatives of the series. |
| format | Article |
| id | doaj-art-8c081edbde684b49a8c7421fc330a5d6 |
| institution | Kabale University |
| issn | 2175-9790 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Universidade de São Paulo |
| record_format | Article |
| series | Brazilian Journal of Pharmaceutical Sciences |
| spelling | doaj-art-8c081edbde684b49a8c7421fc330a5d62025-01-21T07:42:23ZengUniversidade de São PauloBrazilian Journal of Pharmaceutical Sciences2175-97902025-01-016110.1590/s2175-97902025e24510Machine learning based QSAR and Molecular Dynamics simulations in the structural design and mechanism of action of imidazole derivatives with anti-melanoma activityRosalvo Ferreira de Oliveira NetoSérgio Ruschi Bergamachi SilvaCintia Emi Yanaguibashi LealEdilson Beserra de Alencar Filhohttps://orcid.org/0000-0002-1000-0114Abstract Cancer is defined as a group of diseases in which abnormal cells multiply and can invade other organs, requiring continuous studies for new drugs. A series of 177 imidazo[1,2-a]pyridine and imidazo[1,2-a] pyrazine synthetic derivatives were previously obtained, and their anti-melanoma IC50 values have been determined. Here, Artificial Intelligence algorithms were used to select molecular descriptors and build a QSAR model, highlighting structural characteristics related to enhanced molecular potency. Additionally, the imidazopyrazine nucleus was compared to a known inhibitor of the Aurora Kinase enzyme, an important target in cancer therapy. Thus, strategic imidazopyrazines were subjected to comparative molecular dynamics calculations, providing inferences about their possible mechanisms of action. The QSAR model allows for the design and prediction of nine new analogues with favourable predicted IC50 values. Molecular dynamics simulations and the estimated binding energies are consistent with the ranking of activities presented by representatives of the series.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1984-82502025000100335&lng=en&tlng=enImidazole derivativesAnti-melanoma activityBio-inspired algorithmsRandom ForestMolecular DynamicsQSAR |
| spellingShingle | Rosalvo Ferreira de Oliveira Neto Sérgio Ruschi Bergamachi Silva Cintia Emi Yanaguibashi Leal Edilson Beserra de Alencar Filho Machine learning based QSAR and Molecular Dynamics simulations in the structural design and mechanism of action of imidazole derivatives with anti-melanoma activity Brazilian Journal of Pharmaceutical Sciences Imidazole derivatives Anti-melanoma activity Bio-inspired algorithms Random Forest Molecular Dynamics QSAR |
| title | Machine learning based QSAR and Molecular Dynamics simulations in the structural design and mechanism of action of imidazole derivatives with anti-melanoma activity |
| title_full | Machine learning based QSAR and Molecular Dynamics simulations in the structural design and mechanism of action of imidazole derivatives with anti-melanoma activity |
| title_fullStr | Machine learning based QSAR and Molecular Dynamics simulations in the structural design and mechanism of action of imidazole derivatives with anti-melanoma activity |
| title_full_unstemmed | Machine learning based QSAR and Molecular Dynamics simulations in the structural design and mechanism of action of imidazole derivatives with anti-melanoma activity |
| title_short | Machine learning based QSAR and Molecular Dynamics simulations in the structural design and mechanism of action of imidazole derivatives with anti-melanoma activity |
| title_sort | machine learning based qsar and molecular dynamics simulations in the structural design and mechanism of action of imidazole derivatives with anti melanoma activity |
| topic | Imidazole derivatives Anti-melanoma activity Bio-inspired algorithms Random Forest Molecular Dynamics QSAR |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1984-82502025000100335&lng=en&tlng=en |
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