使用基于分类的QSAR模型,分子对接,分子动力学和ADME研究对Fyn激酶抑制剂进行in silico选
Nguyen Thu Hang1, Thai Doan Hoang Anh1, Le Nguyen Thanh2
1Department of Pharmacognosy, Faculty of Pharmacognosy and Traditional Medicine, Hanoi University of Pharmacy, Hanoi, 11000, Vietnam.
Molecular diversity
|June 17, 2024
概括
计算方法确定了9种潜在的Fyn激酶抑制剂. 一个经过验证的QSAR模型,分子对接和ADME研究确定了有前途的化合物,其中四个通过分子动力学模拟显示稳定结合.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 生物化学 生物化学
背景情况:
- 芬基因酶是各种细胞过程中的关键调节剂.
- 芬基因酶的失调与癌症等疾病有关.
- 准Fyn激酶是一个治疗机会.
研究的目的:
- 使用多计算方法识别新型Fyn激酶抑制剂.
- 开发和验证Fyn激酶活性的预测QSAR模型.
- 评估潜在抑制剂的药物相似性和结合稳定性.
主要方法:
- 使用XGBoost的定量结构-活动关系 (QSAR) 模型开发.
- 分子对接模拟以预测结合亲和力和相互作用.
- 吸收,分布,新陈代谢和分泌 (ADME) 概况.
- 分子动力学 (MD) 模拟用于稳定性评估.
主要成果:
- 开发了一种具有0.95准确度的QSAR模型,用于Fyn激酶抑制.
- 九种化合物被确定为潜在的Fyn激酶抑制剂.
- 在MD模拟中,四种化合物 (728,734,736,852) 显示与Fyn激酶稳定结合.
- 为简化Fyn激酶抑制剂查创建了一个基于网络的平台.
结论:
- 综合计算策略有效地识别了潜在的Fyn激酶抑制剂.
- 化合物728,734,736和852是进一步研究的有希望的候选物.
- 开发的QSAR模型和查平台可以加速Fyn酶的药物发现工作.
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