机器学习模型和基于结构的抗菌药物 发现了关键的ABC载体糖结合蛋白A
Anupama Binoy1, Ratul Bhowmik1, Preena S Parvathy1
1Bioinformatics and Computational Biology Lab, Amrita School for Nanosciences and Molecular Medicine, Amrita Vishwa Vidyapeetham, Kochi, Kerala, India.
Journal of cellular biochemistry
|June 30, 2025
概括
机器学习辅助的定量结构-活性关系 (ML-QSAR) 模型被开发用于预测抗菌药物对抗耐药细菌的活性. 最好的模型确定了基于氨酸的新型抑制剂,具有开发新药的潜力.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 耐多药格拉姆阴性细菌感染对全球健康构成重大威胁.
- 开发新的抗菌剂对于打击耐药性至关重要.
- 机器学习辅助的定量结构-活性关系 (ML-QSAR) 为高效的药物发现提供了一个有希望的策略.
研究的目的:
- 开发可靠的ML-QSAR模型,用于预测基诺林基化合物对MsbA的抑制活性.
- 为了确定新型,强效的MsbA抑制剂,用于对抗多药耐药的грам阴性细菌.
- 创建一个用户友好的Web应用程序,用于预测MsbA抑制活性.
主要方法:
- 利用遗传函数近似 (GFA),支持向量机 (SVM) 和人工神经网络 (ANN) 进行ML-QSAR模型开发.
- 采用了8个分子描述符和279个分子指纹来构建预测模型.
- 通过内部,外部和适用性领域分析验证模型的稳定性.
主要成果:
- 基于分子指纹的SVM模型表现出卓越的性能,R2 = 0.9981和q2 = 0.7584.
- 确定了31和40的化合物具有高度活性,从而产生了62种新的化合物.
- 产生了三种基于类素的新型抑制剂 (M28,N7,N23) 具有出色的预测生物活性,结合亲和力和药理动力学特征.
结论:
- 开发并验证了有效的ML-QSAR模型来预测MsbA抑制活性.
- 成功确定了有前途的基于诺林的新型抗菌药物候选者.
- 推出了 MsbA-Pred 网络应用程序,以促进进一步的研究和药物发现工作.
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