Machine learning based QSAR and Molecular Dynamics simulations in the structural design and mechanism of action of imidazole derivatives with anti-melanoma activityAuthorshipSCIMAGO INSTITUTIONS RANKINGS
DOI:
https://doi.org/10.1590/Keywords:
Imidazole derivatives, Anti-melanoma activity, Bio-inspired algorithms, Random Forest, Molecular Dynamics, QSARAbstract
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.
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