相关实验视频
可靠的化合物-蛋白质相互作用预测与可解释和合规的交叉注意力转换器
Peiyao Li1,2, Lan Hua2, Ye Liu2
1Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China.
Journal of chemical information and modeling
|March 6, 2026
概括
ConfBiXtCPI通过提供可靠的化合物-蛋白质相互作用预测来增强药物发现. 这种可解释的深度学习框架提供了不确定性量化,并控制了虚假发现率,以实现高效的实验验证.
科学领域:
- 计算化学是一种计算化学.
- 机器学习在药物发现中的作用
- 生物信息学是一种生物信息学.
背景情况:
- 深度学习加速了虚拟选,但缺乏可靠性保证.
- 化合物-蛋白相互作用 (CPI) 预测至关重要,但面临着不平衡和杂数据的挑战.
- 当前的模型经常充当"黑子",阻碍信任和实验验证.
研究的目的:
- 引入 ConfBiXtCPI,这是一个用于准确,可解释和不确定性量化的CPI预测的综合框架.
- 解决数据不平衡,提高药物发现中的预测可靠性.
- 在虚拟选中实现对虚假发现率的原则控制.
主要方法:
- 开发了一种双向交叉注意力变压器,用于序列级分子识别.
- 整合了蒙德里安符合性预测,以保证跨不平衡数据集的覆盖范围.
- 实施了对受控虚假发现率的合规选择程序.
主要成果:
- 在多个CPI预测基准上实现了最先进的准确性.
- 通过注意地图定位到绑定站点来证明机械解释性.
- 展示了不确定性量化,支持有效的积极学习策略.
结论:
- ConfBiXtCPI为药物发现提供了一个可靠和实用的工具.
- 该框架统一了准确性,可解释性和严格的不确定性量化.
- 能够进行高效的实验验证,并加速新疗法的发现.
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