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In Silico Design and Evaluation of Novel Triazole-Based Compounds as Promising Drug Candidates Against Breast Cancer

机译:在计算机上设计和评估基于三唑的新型化合物作为抗乳腺癌的候选药物

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Computational development of novel triazole-based aromatase inhibitors (AIs) was carried out followed by investigation of the possible interaction modes of these compounds with the enzyme and prediction of the binding affinity by tools of molecular modeling. In doing so, in silico design of potential AIs candidates fully satisfying the Lipinski's "rule of five" was performed using the concept of click chemistry. Complexes of these drug-like molecules with the enzyme were then simulated by molecular docking and optimized by semiempir-ical quantum chemical method PM7. To identify the most promising compounds, stability of the PM7-based ligand/aromatase structures was estimated in terms of the values of binding free energies and dissociation constants. At the final stage, structures of the top ranking compounds bound to aromatase were analyzed by molecular dynamic simulations and binding free energy calculations. As a result, eight hits that specifically interact with the aromatase catalytic site and exhibit the high-affinity ligand binding were selected for the final analysis. The selected AIs candidates show strong attachment to the enzyme active site, suggesting that these small drug-like molecules may present good scaffolds for the development of novel potent drugs against breast cancer.
机译:进行了新的基于三唑的芳香酶抑制剂(AIs)的计算开发,然后研究了这些化合物与酶的可能相互作用方式,并通过分子建模工具预测了结合亲和力。这样做,使用点击化学的概念进行了完全满足Lipinski的“五个规则”的潜在AI候选人的计算机设计。然后通过分子对接模拟这些药物样分子与酶的复合物,并通过半经验量子化学方法PM7对其进行优化。为了鉴定最有希望的化合物,根据结合自由能和解离常数的值估算了基于PM7的配体/芳香化酶结构的稳定性。在最后阶段,通过分子动力学模拟和结合自由能计算来分析与芳香化酶结合的顶级化合物的结构。结果,选择了八种与芳香酶催化位点特异性相互作用并表现出高亲和力配体结合的命中物进行最终分析。选定的AI候选物显示出对酶活性位点的强烈附着,表明这些小的药物样分子可能为开发新型有效的抗乳腺癌药物提供了良好的支架。

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