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有证据的基于深度学习的药物向相互作用预测
Yanpeng Zhao1,2, Yuting Xing3, Yixin Zhang1
1Academy of Military Medical Sciences, Beijing, China.
Nature communications
|July 27, 2025
概括
使用证据深度学习 (EDL) 的新方法EviDTI通过量化不确定性来改善药物向相互作用 (DTI) 的预测. 这种方法通过优先考虑实验验证的可靠预测来提高药物发现效率.
科学领域:
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
- 人工智能在药物发现中的作用
背景情况:
- 药物向相互作用 (DTI) 的预测对于药物发现至关重要.
- 目前用于DTI预测的深度学习模型在不确定性量化,稳定性和过度自信方面面临挑战.
研究的目的:
- 引入EviDTI,一种使用证据深度学习 (EDL) 在DTI预测中量化不确定性的新方法.
- 提高DTI预测模型的可靠性和稳定性.
- 通过优先考虑高可信度预测来加速药物发现.
主要方法:
- EviDTI集成了多个数据维度:药物2D/3D结构和目标序列.
- 使用证据深度学习 (EDL) 来估计神经网络预测中的不确定性.
- 根据三个基准数据集对11个基线模型进行评估.
主要成果:
- 与现有的DTI预测模型相比,EviDTI的表现具有竞争力.
- 该方法有效校准预测错误,提供可靠的不确定性估计.
- 在一项案例研究中,以不确定性为指导的预测成功地确定了氨酸激酶FAK和FLT3的新型潜在调节剂.
结论:
- 有证据的深度学习 (EDL) 为DTI预测中的不确定性量化提供了一个强大的框架.
- EviDTI提高了药物发现管道的效率和可靠性.
- 这种方法对加速识别新型候选药物具有重大意义.
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