使用几个人工智能算法预测复合目标相互作用,并与基于共识的战略进行比较.
Karina Jimenes-Vargas1,2, Alejandro Pazos3,4,5, Cristian R Munteanu3,4,5
1Bio-Cheminformatics Research Group, Universidad de Las Américas, Quito, 170504, Ecuador. karina.jimenes@udla.edu.ec.
Journal of cheminformatics
|March 6, 2024
概括
预测蛋白质点对于药物发现至关重要. 这项研究表明,开发的以目标为中心的模型 (TCM) 优于现有的网络工具,特别是在使用共识策略以提高准确性时.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现
背景情况:
- 准确地预测化学化合物的蛋白质标对于理解作用机制,副作用和推动药物发现至关重要.
- 现有的用于预测化合物-目标相互作用的计算模型在性能上有所不同.
研究的目的:
- 开发和评估新的以目标为中心的模型 (TCM) 来预测化合物-蛋白质相互作用.
- 将内部开发的TCM与公开可用的网络工具 (WTCM) 的性能进行比较.
- 研究共识策略在提高预测准确性的有效性.
主要方法:
- 开发15个以目标为中心的模型 (TCM),使用多种分子描述和机器学习算法.
- 评估和与17个第三方网络工具 (WTCM) 的比较.
- 实施和评估共识策略,以汇总预测.
主要成果:
- 开发的TCM实现了f1得分大于0.8.8的值.
- 最好的TCM在关键指标 (TPR,TNR,FNR,FPR) 中表现优于最好的WTCM.
- 共识策略显著提高了预测相关性,TCM共识达到0.98 TPR和0 FNR.
结论:
- 以目标为中心的模型,特别是在采用共识策略时,与现有的网络工具相比,在复合目标交互预测方面提供了更好的性能.
- 开发的计算工具和共识方法为药物发现和化学生物学研究提供了宝贵的资源.
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