机器学习和深度学习模型用于预测AmpCβ-Lactamase的非共价抑制剂
Youcef Bagdad1, Marion Sisquellas1, Michel Arthur2
1Université Paris Cité, CNRS UMR 8038 CiTCoM, Inserm U1268 MCTR, 75006 Paris, France.
ACS omega
|October 14, 2024
概括
研究人员开发了机器学习模型,以预测AmpC (一种β-乳糖酶) 的新型非共价抑制剂,以对抗抗生素耐药性 (AR). 这些模型实现了高精度,有助于发现针对耐药细菌的新型治疗方法.
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:
- 抗生素耐药性 (AR) 是一个日益增长的全球健康威胁,由细菌适应机制,如β-乳糖酶合成驱动.
- C类β-乳糖酶 (AmpC) 对关键的β-乳糖抗生素产生了耐药性,需要开发新的治疗策略.
研究的目的:
- 开发精确的机器学习 (ML) 和深度学习 (DL) 模型,用于预测AmpCβ-lactamase的非共价抑制剂.
- 识别具有AMPC抑制剂潜力的新型化学实体,以解决紧急治疗需求.
主要方法:
- 使用大型复合数据集进行培训和验证.
- 开发并比较了支持矢量机 (SVM),随机森林 (RF) 和前神经网络 (FFNN) 分类模型.
- 分析了已识别的抑制剂的物理化学特性和预测的结合方式.
主要成果:
- 单个模型的交叉验证准确度从80%到82%不等.
- 结合的ML/DL模型在预测非共价AMPC抑制剂方面达到83%的整体准确性.
- 确定了与抑制剂设计相关的关键物理化学特征和结合模式.
结论:
- 开发的ML/DL模型为虚拟查和识别新型非共价AMPC抑制剂提供了强大而准确的平台.
- 这些预测模型是加速发现新解决方案的有价值的工具,以应对beta-lactamase介导的抗生素耐药性的不断升级的挑战.
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