在药物发现中实现知情决策:使用基于神经网络的结构-活性模型进行全面的校准研究
Hannah Rosa Friesacher1,2, Ola Engkvist3,4, Lewis Mervin5
1Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, 3000, Belgium. rosa.friesacher@kuleuven.be.
Journal of cheminformatics
|March 5, 2025
概括
计算模型通过预测相互作用来加速药物发现. 本研究引入了贝叶斯方法 (HBLL) 来改进不确定性估计,提高模型校准和准确性,以便更好地做出决策.
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
- 生物制药的发展.
背景情况:
- 药物发现依赖于预测计算模型,但由于校准差,它们的不确定性估计往往不可靠.
- 准确的不确定性量化对于治疗剂开发中的风险评估和决策至关重要.
研究的目的:
- 为了比较模型选择策略来实现校准良好的神经网络.
- 引入和评估HMC贝叶斯最后层 (HBLL) 方法,以高效地估计贝叶斯不确定性.
- 评估将后期校准与不确定性量化方法相结合的影响.
主要方法:
- 对超参数调整指标 (精度,校准分数) 的比较分析.
- 使用哈密尔顿蒙特卡洛 (HMC) 采样实施HMC贝叶斯最后层 (HBLL) 方法.
- 后期校准技术与不确定性量化方法的整合.
主要成果:
- 与基线神经网络相比,HBLL方法显著改善了模型校准.
- HBLL的性能与现有的不确定性量化方法相提并论.
- 将后期校准与不确定性量化相结合,可以提高模型准确性和校准.
结论:
- HBLL方法提供了一种有效的方法,用于提高药物发现预测模型中的不确定性估计.
- 精确校准的模型对于治疗开发中的可靠风险评估至关重要.
- 结合校准和不确定性定量化的混合方法产生了卓越的模型性能.
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