排序和切割:对于扩展连接指纹的基于哈希的折叠来说,这是一个简单而优质的替代方案
Markus Dablander1, Thierry Hanser2, Renaud Lambiotte1
1Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter (550), Woodstock Road, Oxford, OX2 6GG, UK.
Journal of cheminformatics
|December 4, 2024
概括
排序和切割 (Sort & Slice) 是一种用于向量化扩展连接指纹 (ECFP) 的新方法,其性能优于分子机器学习的传统基于哈希的折叠. 这种技术通过有效地汇集子结构信息来改善化学预测.
科学领域:
- 化学信息学 化学信息学
- 分子机器学习分子机器学习
- 计算化学的计算化学
背景情况:
- 扩展连接指纹 (ECFP) 广泛用于化学信息学和机器学习中的分子表示.
- 目前用于将ECFP子结构转换为比特矢量的方法,如基于哈希的折叠,可能导致信息丢失和比特碰撞.
- 图形神经网络提供先进的原子特征聚合,但ECFP向量化仍然是一个瓶.
研究的目的:
- 为结构指纹矢量化引入一个通用的数学框架,称为子结构聚合.
- 介绍Sort & Slice,这是一种新的,没有比特碰撞的替代方案,用于ECFP聚合的基于哈希的折叠.
- 通过计算评估Sort & Slice与分子性质预测的现有方法对比.
主要方法:
- 开发了基础结构聚合的一般框架,包括各种矢量化技术.
- 实现了Sort & Slice,它根据流行率对子结构进行排名,并选择顶部的L特征.
- 使用基于ECFP的分子性质预测任务,比较Sort & Slice与基于哈希的折叠,过和相互信息最大化.
主要成果:
- 在各种预测任务中,Sort & Slice在基于哈希的折叠上表现出强大而实质性的性能改进.
- 提出的方法始终优于其他研究的亚结构聚合技术.
- 在不同的机器学习模型,数据分割策略和ECFP超参数中观察到性能增长.
结论:
- 排序和切割是一种简单但非常有效的方法来向量化ECFP,为基于哈希的折叠提供了一个优质的替代方案.
- 子结构聚合框架为结构指纹矢量化提供了通用的方法.
- 在监督分子机器学习中,Sort & Slice被推作为向量化ECFP的默认方法,以提高预测准确度.
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