质量胜过数量:如何在使用对接来重新利用时获得最佳结果.
Lenin Domínguez-Ramírez1, Maricruz Anaya-Ruiz2, Paulina Cortés-Hernández1
1Computational Biochemistry Laboratory, Centro de Investigación Biomédica de Oriente (CIBIOR), Instituto Mexicano del Seguro Social (IMSS), Atlixco, Mexico.
Frontiers in bioinformatics
|June 10, 2025
概括
评估了分子对接程序,以识别候选药物. 使用卷积神经网络 (CNN) 评分的GNINA在对基化酶5A (PDE5A) 的已知配体进行排名方面表现出色,提高了药物发现的准确性.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 分子对接是研究蛋白质-连接体相互作用的关键计算方法.
- 评估分子对接结果的可靠性和准确性仍然是一个挑战.
- 确定治疗标的有效配体对于药物开发至关重要.
研究的目的:
- 为了比较八个免费分子对接程序的性能.
- 评估这些程序识别人类固酶5A (PDE5A) 标的已知配体的能力.
- 评估卷积神经网络 (CNN) 在提高对接结果质量的评分中的实用性.
主要方法:
- 使用八个不同的对接程序对PDE5A目标进行药物库的选.
- 使用接收器操作特征 (ROC) 分析来评估程序的特异性和敏感性.
- 实施CNN分数切断以提高高质量的对接结果的选择.
- 将对接亲和度得分与CNN得分进行比较,以排名潜在的药物结合剂.
主要成果:
- 在识别已知的PDE5A配体方面,GNINA表现出卓越的性能,特别是在使用其CNN评分时.
- 所有测试的对接套件都在特异性方面表现出局限性,经常错误地识别非结合剂.
- 应用0.9的CNN分数截止值显著提高了结果的特异性,对灵敏度的影响最小.
- 切断后精制的数据集较小,但质量更高,促进了更好的候选人选择.
结论:
- GNINA的CNN评分为改善从分子对接屏幕中对潜在药物候选者的排名和选择提供了宝贵的工具.
- 结合ROC分析和CNN分数切断的启发式方法可以提高药物发现分子对接的可靠性.
- 拟议的方法有助于通过有效过假阳性来产生更相关的对接结果.
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