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基于机器学习和分子模拟的协议,以确定针对艾滋病毒感染的逆转录酶的新型潜在抑制剂

Muhammad Shahab¹, Guojun Zheng¹, Yousef A Bin Jardan²

¹State Key Laboratories of Chemical Resources Engineering, Beijing University of Chemical Technology, Beijing, PR China.

Journal of biomolecular structure & dynamics

|February 21, 2024

概括

机器学习模型通过针对逆转录酶来识别获得性免疫缺陷综合征 (艾滋病) 的潜在候选药物. 随机森林模型显示了86%的准确性,导致选择了三个有前途的化合物进行进一步研究.

科学领域:

计算化学和药物发现
机器学习在药理学中的应用.

背景情况:

获得性免疫缺陷综合征 (AIDS) 是一种由人类免疫缺陷病毒 (HIV) 引起的严重疾病.
抑制逆转录酶活性是治疗艾滋病的关键治疗策略.

研究的目的:

开发和评估用于识别新型逆转录酶抑制剂的机器学习模型.
选化合物潜在的抗艾滋病毒药物开发.

主要方法:

利用了机器学习算法,包括支持向量机 (SVM),k-最近邻居 (k-NN),随机森林 (RF) 和高斯素朴贝叶斯 (GNB).
在BindingDB的5159种化合物的数据集上训练模型,其中1645种对逆转录酶有活性,3514种对逆转录酶无活性.
使用十倍交叉验证验证模型并将表现最好的模型 (RF) 应用于外部ZINC数据集.

主要成果:

随机森林模型在对逆转录酶的化合物活性预测方面达到86%的准确性.
三种化合物 (ZINC1359750464,ZINC1435357562,ZINC1545719422) 根据利宾斯基规则,对接分数和相互作用分析进行了选择.
分子动力学模拟和MM/GBSA表明选择的化合物与逆转录酶酶的稳定相互作用.

结论:

机器学习,特别是随机森林算法,在识别潜在的HIV逆转录酶抑制剂方面是有效的.
这些已识别的化合物代表了开发新的艾滋病治疗方法的有希望的线索.

关键词:

模拟MD的模拟方法机器学习机器学习停靠的对接方式反向转录酶的使用虚拟选虚拟选虚拟选

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