数据对使用深度神经网络的基于结构的绑定亲和预测的影响
Pierre-Yves Libouban1, Samia Aci-Sèche1, Jose Carlos Gómez-Tamayo2
1Institute of Organic and Analytical Chemistry (ICOA), UMR7311, Université d'Orléans, CNRS, Pôle de Chimie rue de Chartres, 45067 Orléans, CEDEX 2, France.
对于药物发现的深度学习模型,蛋白质-连接体结合的预测可能被高估了. 使用更多数据进行训练,而不仅仅是高质量的数据,并考虑绑定口袋大小可以提高性能,并揭示测试集偏差.
科学领域:
- 计算化学是一种计算化学.
- 生物化学 生物化学
- 人工智能在药物发现中的作用
背景情况:
- 深度学习 (DL) 算法对于预测药物发现中的蛋白质-连接体结合亲和关系至关重要.
- 当前DL模型在亲和预测中的表现已经停滞不前,引发了对数据偏差和高估的担忧.
研究的目的:
- 调查输入数据参数对神经网络亲和力预测模型的影响.
- 确定影响药物发现中的DL模型性能的关键因素.
主要方法:
- 神经网络亲和力预测模型的分析.
- 评估输入数据参数,包括绑定口袋大小和数据集质量.
- 使用各种评估方法对模型性能进行基准测试和比较.
主要成果:
- 绑定口袋大小是影响模型性能的一个关键因素.
- 使用更大的数据集进行培训,即使不是完全高质量的培训,也比限制的高质量培训更有益.
- 在通常用于DL亲和力预测的测试集中确认偏差.
结论:
- 目前用于DL亲和力预测的性能指标可能由于数据偏差而过于乐观.
- 更全面的评估和基准测试是必要的,以了解模型决策.
- 优化训练数据策略和考虑结构特征,如绑定口袋大小,对于推动药物发现中的DL至关重要.
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