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基于机器学习,拓CoMFA和分子对接的VEGFR-2抑制剂的QSAR分析

Hao Ding¹, Fei Xing², Lin Zou³

¹Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, 110004, Liaoning, China.

|March 30, 2024

概括

机器学习确定了五种新的血管内皮生长因子受体2 (VEGFR-2) 抑制剂,用于对抗癌症. 这些化合物与VEGFR-2标具有强烈的相互作用,为现有药物提供了具有较少副作用的有希望的替代品.

科学领域:

药用化学医学化学
计算机化药物发现技术
在瘤学瘤学.

背景情况:

血管内皮生长因子受体2 (VEGFR-2) 激酶抑制剂对于向癌症血管生成至关重要.
现有的VEGFR-2抑制剂与不良影响有关,包括皮肤毒性,胃肠道问题和肝功能障碍.

研究的目的:

开发新的VEGFR-2抑制剂,提高疗效,并可能减少副作用.
使用机器学习和计算方法构建强大的结构-活动关系 (SAR) 模型.

主要方法:

利用机器学习算法和拓对比分子场分析 (CoMFA) 进行二维和三维定量结构-活动关系 (QSAR) 建模.
使用分子对接来评估候选化合物与VEGFR-2标蛋白的结合亲和力和相互作用.

主要成果:

使用K-近邻 (KNN) 实现了2D-SAR模型 (82.4%的训练,80.1%的测试集) 的高预测准确度.
开发了一个稳定的3D-QSAR模型,交叉验证系数 (q2) 大于0.5.5.
鉴定了五种具有强烈键相互作用的强效化合物与VEGFR-2,其总对接得分高于6.

结论:

成功使用机器学习和拓CoMFA来识别五种新的潜在VEGFR-2抑制剂.
这些新型化合物代表了对抗癌症武器库的有希望的补充,可能提供更好的安全性.

关键词:

机器学习是机器学习.分子对接是分子对接.结构活动关系模型模型.在VEGFR-2的基础上,

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