使用机器学习和QSAR方法设计强大的HIV-1蛋白酶抑制剂
Saba Ali1, Ismail Dwi Putra2, Hathaichanok Chuntakaruk3
1Center of Excellence in Computational Chemistry (CECC), Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand.
ACS omega
|January 1, 2026
概括
研究人员使用机器学习和QSAR模型开发了新的人类免疫缺陷病毒1型 (HIV-1) 蛋白酶抑制剂. 这些新型化合物在克服药物耐药性方面表现有前途,优于对抗HIV-1变体的现有治疗方法.
科学领域:
- 药用化学 医学化学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 由人类免疫缺陷病毒1型 (HIV-1) 引起的获得性免疫缺陷综合征 (AIDS) 是一个持续的全球健康挑战.
- 药物耐药性,特别是对达鲁纳维尔等蛋白酶抑制剂的耐药性,限制了当前抗逆转录病毒疗法对HIV-1变体的有效性.
研究的目的:
- 通过整合机器学习 (ML) 和定量结构-活性关系 (QSAR) 建模来设计新的,强大的HIV-1蛋白酶抑制剂.
- 确定关键的分子描述物和结构特征,这些特征有助于抑制HIV-1蛋白酶的活性.
- 评估新设计的抑制剂对野生类型和耐性HIV-1菌株的结合潜力.
主要方法:
- 使用各种分子描述器开发和比较多个QSAR模型 (GFA,MLR,RF,GBR,XGBoost).
- 应用SHAP分析来解释模型预测,并确定影响pIC50.50的关键分子特征.
- 基于结构的分子对接模拟,以评估预测的抑制剂与HIV-1蛋白酶的结合亲和力和相互作用.
主要成果:
- 梯度增强回归器 (GBR) 模型在预测抑制活性方面取得了高准确性 (R2 = 0.911和0.994).
- 关键的预测特征包括电子电荷在C53,低二极极时刻,以及短C53-O54债券长度.
- 设计了五种强效抑制剂 (B01-B05),其中B03和B05通过疏水和键强烈地与野生型和变体HIV-1蛋白酶结合.
结论:
- 综合的QSAR-ML和基于结构的方法成功地确定了有希望的新型HIV-1蛋白酶抑制剂候选者.
- 设计的化合物显示出在HIV-1治疗中克服现有的耐药性机制的潜力.
- 这项研究为开发下一代抗逆转录病毒疗法来对抗抗性HIV-1菌株提供了基础.
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