基准测试主动学习协议用于干结合亲和力预测
Rohan Gorantla1,2,3, Alžbeta Kubincová3, Benjamin Suutari3
1School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, U.K.
Journal of chemical information and modeling
|March 6, 2024
概括
积极学习 (AL) 通过系统评估机器学习模型和参数来优化药物发现. 高斯过程模型在稀疏数据方面表现出色,而更大的初始批量大小可以改善顶部粘合剂识别.
科学领域:
- 计算化学是一种计算化学.
- 化学信息学 化学信息学
- 机器学习在药物发现中的作用
背景情况:
- 积极学习 (AL) 对于从大型分子图书馆中识别强效药物候选者至关重要.
- 了解AL参数的影响对于设计有效的药物发现协议至关重要.
研究的目的:
- 系统地评估机器学习模型,样本选择和批量大小在药物发现的积极学习中.
- 评估数据集特征和噪声对AL性能的影响.
主要方法:
- 利用四个亲和数据集 (TYK2,USP7,D2R,Mpro) 来测试高斯过程 (GP) 和Chemprop模型.
- 使用R2,斯皮尔曼等级,RMSE,召回和F1得分等指标评估性能.
- 评估了初始和随后的批量大小和人工数据噪声的影响.
主要成果:
- 在稀疏的数据集上,GP模型的性能优于Chemprop;在较大的数据集上,性能相当.
- 较大的初始批量大小改善了顶部粘合剂识别 (回忆) 和整体相关性.
- 较小的批量大小 (20-30个化合物) 是随后的AL周期的最佳选择.
- 适度的人工噪音没有妨碍,但过度的噪音会对性能产生负面影响.
结论:
- 选择AL模型和参数显著影响药物发现结果.
- 优化批量大小和考虑数据稀疏性对于强大的主动学习协议至关重要.
- 即使在适度的数据噪声下,AL仍然有效,但过度的噪声会损害其实用性.
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