Sort & Slice：一种替代基于哈希的折叠方法用于扩展连通性指纹的简单且更优的方案
Sort & Slice: A Simple and Superior Alternative to Hash-Based Folding for Extended-Connectivity Fingerprints
AI总结：
提出Sort & Slice子结构池化方法，通过排序并选取最频繁子结构生成无位碰撞指纹，在分子属性预测中稳健优于传统哈希折叠等方案，建议作为有监督分子机器学习的默认向量化技术。
摘要：
扩展连通性指纹（ECFPs）是当前化学信息学和分子机器学习中无处不在的工具，也是用于化学预测的最普遍的分子特征提取技术之一。图神经网络学习的原子特征可以通过多种图池化方法聚合为化合物级别的表示；相比之下，检测到的ECFP子结构集合默认仅通过简单的基于哈希的折叠过程转换为位向量。我们引入了一个通用的数学框架，通过一种称为子结构池化的正式操作来实现结构指纹的向量化，该操作涵盖了基于哈希的折叠、算法子结构选择以及多种其他潜在技术。接着，我们描述了Sort & Slice，这是一种易于实现且无位碰撞的替代方法，用于ECFP子结构的池化。Sort & Slice首先根据ECFP子结构在给定训练化合物集中的相对普遍性对其进行排序，然后切除所有子结构，仅保留$L$个最频繁出现的子结构，随后用于生成所需长度为$L$的二进制指纹。我们在基于ECFP的分子属性预测中，计算并比较了基于哈希的折叠、Sort & Slice以及两种高级有监督子结构选择方案（过滤和互信息最大化）的性能。结果表明，尽管技术简单，Sort & Slice在预测任务、数据划分技术、机器学习模型和ECFP超参数方面，稳健地（有时是显著地）优于传统的基于哈希的折叠以及其他研究方法。因此，我们建议将Sort & Slice作为默认的子结构池化技术，规范地替代基于哈希的折叠，用于向量化ECFPs以进行有监督分子机器学习。
英文摘要：
Extended-connectivity fingerprints (ECFPs) are a ubiquitous tool in current cheminformatics and molecular machine learning, and one of the most prevalent molecular feature extraction techniques used for chemical prediction. Atom features learned by graph neural networks can be aggregated to compound-level representations using a large spectrum of graph pooling methods; in contrast, sets of detected ECFP substructures are by default transformed into bit vectors using only a simple hash-based folding procedure. We introduce a general mathematical framework for the vectorisation of structural fingerprints via a formal operation called substructure pooling that encompasses hash-based folding, algorithmic substructure-selection, and a wide variety of other potential techniques. We go on to describe Sort & Slice, an easy-to-implement and bit-collision-free alternative to hash-based folding for the pooling of ECFP substructures. Sort & Slice first sorts ECFP substructures according to their relative prevalence in a given set of training compounds and then slices away all but the $L$ most frequent substructures which are subsequently used to generate a binary fingerprint of desired length, $L$. We computationally compare the performance of hash-based folding, Sort & Slice, and two advanced supervised substructure-selection schemes (filtering and mutual-information maximisation) for ECFP-based molecular property prediction. Our results indicate that, despite its technical simplicity, Sort & Slice robustly (and at times substantially) outperforms traditional hash-based folding as well as the other investigated methods across prediction tasks, data splitting techniques, machine-learning models and ECFP hyperparameters. We thus recommend that Sort & Slice canonically replace hash-based folding as the default substructure-pooling technique to vectorise ECFPs for supervised molecular machine learning.
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