一种数据驱动的机器学习方法,用于发现强大的LasR抑制剂.
Christabel Ming Ming Koh1, Lilian Siaw Yung Ping1, Christopher Ha Heng Xuan1
1Faculty of Engineering, Computing, and Science, Swinburne University of Technology, Sarawak, Malaysia.
Bioengineered
|August 8, 2023
概括
机器学习通过准LasR定数感应系统来识别潜在的药物来对抗多药耐药Pseudomonas aeruginosa. 开发的算法准确地预测了LasR抑制剂,为对抗持久性感染提供了新的治疗途径.
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.
- 抗微生物耐药性研究的研究.
背景情况:
- 耐多药性Pseudomonas aeruginosa是一种严重的全球健康威胁.
- 新抗生素的有限开发需要替代治疗策略.
- 针对 las 定决数传感 (QS) 系统是一个有前途的方法来对抗P. aeruginosa.
研究的目的:
- 开发一种基于机器学习的药物预测算法,用于识别强大的LasR抑制剂.
- 选化学数据库寻找针对P. aeruginosa las QS系统的新型化合物.
- 通过计算分析验证潜在的抑制剂.
主要方法:
- 使用AdaBoostM1.1.开发和优化一个多层感知器 (MLP) 算法.
- 使用5倍交叉验证和测试集评估模型性能.
- 虚拟选Enamine数据库和随后的分子对接,分子动力学,MM-GBSA和自由能量景观分析.
主要成果:
- 最好的MLP模型在区分LasR抑制剂中实现了90.7%的准确性,AUC为0.95,MCC为0.81.
- 虚拟选发现了几个排名最高的化合物,与naringenin相比,它们具有更高的预测配体结合亲和力.
- 六个顶级热门中的五个被预测为强大的LasR抑制剂,具有潜在的治疗应用.
结论:
- 这项研究首次评估了基于MLP的定量结构-活性关系 (QSAR) 模型,用于发现LasR抑制剂.
- 开发的模型有效地识别了针对P. aeruginosa的潜在候选药物.
- 这些已识别的化合物代表了开发P. aeruginosa感染新治疗方法的有希望的线索.
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