机器学习方法识别特定阶段的抗疟疾化合物的化学特征
Ashleigh van Heerden1, Gemma Turon2, Miquel Duran-Frigola2
1Department of Biochemistry, Genetics and Microbiology, Institute for Sustainable Malaria Control, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa.
ACS omega
|November 29, 2023
概括
机器学习模型识别了用于抗疟疾药物发现的化学特征. 这些模型预测了对无性血液阶段 (ABS) 寄生虫和性细胞的活性,有助于开发新的疟疾治疗方法.
科学领域:
- 药用化学 医学化学
- 寄生虫学的寄生虫学
- 计算生物学 计算生物学
背景情况:
- 抗疟疾药物的发现依赖于对各种化学库进行查,以对抗Plasmodium falciparum.
- 了解与对抗不同寄生虫阶段 (无性血液阶段和性细胞) 活动相关的化学特征至关重要.
- 识别非活性化合物的特征可以防止浪费查工作.
研究的目的:
- 应用机器学习来识别与特定阶段的抗疟疾活动相关的化学空间.
- 定义与无性血液阶段 (ABS) 活性和性细胞杀菌活性相关的化学特征.
- 识别不活性化合物的化学特性,以改进药物查策略.
主要方法:
- 从大型化学图书馆收集的疗效数据,对Plasmodium falciparum的无性和性阶段进行了选.
- 为化合物计算的分子指纹和训练的机器学习模型.
- 利用支持矢量机器 (SVM) 预测化合物活性和识别关键化学特征.
主要成果:
- 开发了强大的机器学习模型,能够预测抗疟疾活性对抗ABS和游戏细胞.
- 使用SVM.实现了高回忆率 (90%的ABS,66%的性细胞) 和低的错误阳性率 (15%的ABS,1%的性细胞).
- 鉴定了活性和非活性化合物种群中丰富的化学特征,对于优化抗疟疾候选药物至关重要.
结论:
- 机器学习有效地识别了特定阶段的抗疟疾活性和相关的化学特征.
- 开发的模型在各种化学空间中都很强大,作为一个有价值的优先级工具.
- 这种方法可以简化到的优化,并指导未来的抗疟疾药物发现计划.
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