增强CYP450-基结合预测:基于基和混合机器学习模型的比较分析
Anastasiia Tikhonova1,2, Eric Chun Yong Chan2, Hao Fan1,3,4,5,6
1Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore 138671, Republic of Singapore.
Journal of chemical information and modeling
|January 13, 2026
概括
预测细胞染色体P450 (CYP450) 配体结合对于药物发现至关重要. 一个新的混合机器学习模型比现有方法提高了预测准确度,有助于早期安全性和有效性评估.
科学领域:
- 药理学和计算化学
- 药物发现和开发 药物发现和开发
背景情况:
- 细胞染色体P450 (CYP450) 代谢显著影响药物的疗效,安全性和不良反应.
- 实验性确定CYP450 - 配体相互作用是耗时且资源密集的.
- 现有的计算方法,特别是基于联结体的方法,往往忽略了关键的蛋白质-联结体结构细节.
研究的目的:
- 开发一种混合机器学习框架,用于准确预测CYP450配体结合.
- 克服当前计算方法的局限性,以捕获蛋白质 - 连接体结构复杂性.
- 为在早期药物发现中优先考虑CYP450结合评估提供强大的计算工具.
主要方法:
- 开发了一种混合机器学习模型,集成了连接体,蛋白质和蛋白质-连接体相互作用描述器.
- 集成的分子对接参数,复制功能组件和结构相互作用指纹 (SIFt).
- 通过交叉验证对CYP1A2和CYP17A1异型的模型性能进行评估.
主要成果:
- 混合模型与独立的分子对接和基于连接体的方法相比,显示出更高的预测准确性.
- 基准测试显示,与瑞士ADME和ADMETlab 3.0.0.等最先进的工具相比,性能得到了提高.
- 该框架有效地整合了各种数据类型,以改善约束性预测.
结论:
- 开发的混合机器学习框架为CYP450配体结合的预测提供了一种多功能和准确的方法.
- 这种计算工具可以通过高效地优先考虑化合物,显著加速早期药物发现.
- 该研究推进了用于评估药物代谢和安全性概况的计算策略.
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