为了更可靠的顶部对接得分预测,SchNetPack超参数优化
Ján Matúška1, Lukas Bucinsky1, Marián Gall2,3
1Institute of Physical Chemistry and Chemical Physics, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, SK-81237 Bratislava, Slovak Republic.
The journal of physical chemistry. B
|May 11, 2024
概括
SchNetPack模型的超参数调整,特别是切断距离,显著提高了顶部对接分数的预测. 这种方法优于数据采样技术,如过量采样和不足采样,以提高机器学习模型的准确性.
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
背景情况:
- 预测顶级对接分数对于识别有力的药物候选人至关重要.
- 机器学习模型,如使用SchNetPack的机器学习模型,往往难以推断到罕见的,高得分的化合物.
研究的目的:
- 增强SchNetPack神经网络的推断能力,用于预测顶级对接分数.
- 确定提高高得分化合物的预测准确度的关键超参数.
主要方法:
- 对SchNetPack原子模型表示的超参数调整.
- 使用平均平方误差 (MSE) 和损失景观来评估预测稳定性.
- 截止距离,辐射基函数,网络层和特征向量大小的比较.
- 对训练数据的超采样和不足采样技术的分析.
主要成果:
- 优化截止超参数 (在5 Å) 显著改善了顶部对接得分的预测 (MSE从3.5降低到0.9 kcal/mol).
- 其他超参数对预测最高得分的化合物与截止值相比的影响很小.
- 截止优化比数据采样方法 (低采样>高采样) 更能提高最高分数的准确性.
- 当专注于最高得分的化合物时,总体预测准确性略有下降.
结论:
- 切断距离是改善SchNetPack对顶部对接分数的预测最重要的超参数.
- 超参数调整提供了一个比数据采样更有效的策略,用于提高机器学习模型中稀有,高价值化合物的预测.
- 仔细调整可以通过更准确的对接分数预测来改善有前途的候选药物的识别.
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