PmxPred:一种数据驱动的方法,用于识别活跃的多胺类同类物,用于对抗阴性细菌
Xiaoyu Wang1, Nitin Patil2, Fuyi Li3
1Monash Biomedicine Discovery Institute, Monash University, Melbourne, VIC, 3800, Australia; Monash Data Futures Institute, Monash University, Melbourne, VIC, 3800, Australia; Centre to Impact AMR, Monash University, Melbourne, VIC, 3800, Australia.
Computers in biology and medicine
|November 22, 2023
概括
机器学习框架PmxPred加速了对高抗微生物活性,高抗菌活性的多素类型的发现,同时最大限度地减少了毒性. 这种工具有助于开发更安全,更有效的抗生素.
科学领域:
- 计算化学是一种计算化学.
- 机器学习 机器学习
- 药物发现 药物发现
背景情况:
- 耐多药格拉姆阴性细菌构成了全球健康威胁.
- 聚米克辛对于治疗耐药性感染至关重要,但具有显著的毒性和神经毒性.
- 定量结构-活性关系 (QSAR) 和模拟设计是开发更安全药物的关键策略.
研究的目的:
- 开发PmxPred,这是一个机器学习框架,用于预测聚米辛类型的抗菌活性.
- 加速发现具有增强疗效和降低毒性的新型聚胺类相应物.
- 为了阐明机制,确定有助于抗微生物活性的关键特征.
主要方法:
- 使用图形卷积网络 (GCN) 和catBoost机器学习算法开发了PmxPred.
- 使用RDKit描述器进行分子和残留物表示.
- 采用集体学习来预测各种抗微生物活性对抗各种格拉姆阴性细菌.
主要成果:
- 在独立测试数据集上,PmxPred实现了高预测性能,AUROC得分从0.756到0.895不等.
- 该模型与在数百万个分子上训练的转移学习方法相比,表现出更高的性能.
- 特性重要性分析提供了对抗微生物活性机制的见解.
结论:
- PmxPred是预测活性多胺类同类的宝贵工具,有助于开发新的抗生素.
- 该框架有可能阐明聚胺抗菌活性背后的机制.
- 源代码是公开的,促进进一步的研究和开发.
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