值得信赖的CPI:值得信赖的化合物-蛋白质相互作用预测
IEEE transactions on computational biology and bioinformatics
|August 14, 2025
概括
我们开发了TrustworthyCPI,这是一种新的计算方法,可以自信地预测化合物-蛋白相互作用 (CPI). 这种方法通过提供可靠的预测来增强药物发现,与以前的方法不同.
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.
- 机器学习是机器学习.
背景情况:
- 化合物-蛋白相互作用 (CPI) 对药物发现和开发至关重要.
- 传统的CPI识别实验方法耗时且昂贵.
- 现有的计算CPI预测方法缺乏信任度,这在药物研究中构成风险.
研究的目的:
- 开发一种可靠的计算方法来预测化合物-蛋白相互作用 (CPI).
- 为CPI预测提供信心指标,以减轻药物开发中的风险.
- 提高CPI预测模型的准确性和可靠性.
主要方法:
- 开发了一种基于端到端学习的新方法,名为TrustworthyCPI.
- 在复合SMILES和蛋白质序列上使用卷积编码器来进行潜伏表示.
- 一个证据分类器预测相互作用概率和信心水平,通过反向传播同时训练.
主要成果:
- 值得信赖的CPI证明了与最先进的方法相比具有竞争力的预测性能 (ACC,AUC,AUPRC).
- 该方法为其预测提供了可靠的信心指标.
- 涉及SARS-CoV-2 3C-Like Protease (3CLPro) 的案例研究验证了该方法的有效性.
结论:
- 值得信赖的CPI提供了一种可靠和准确的方法来预测化合物-蛋白质相互作用.
- 综合信任度提高了计算预测在药物发现中的实用性.
- 这种方法对识别潜在的候选药物和了解药物机制具有前景.
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