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.

推荐使用 Sort & Slice 作为将连通性指纹（ECFPs）向量化的默认技术，以替代基于哈希折叠的简单技术，该技术通过排序和切片基于训练化合物中的相对普遍性选择特定数量的亚结构，从而在预测任务、数据分割技术、机器学习模型和 ECFP 超参数中稳健地提高性能。

排序与切片：扩展连接指纹的简单且优越替代哈希折叠方法

Sort & Slice: A Simple and Superior Alternative to Hash-Based Folding  for Extended-Connectivity Fingerprints

We introduce a convolutional neural network that operates directly on graphs.
These networks allow end-to-end learning of prediction pipelines whose inputs
are graphs of arbitrary size and shape. The architecture we present generalizes
standard molecular feature extraction methods based on circular fingerprints.
We show that these data-driven features are more interpretable, and have better
predictive performance on a variety of tasks.

本文介绍了一种可直接在图形上操作的卷积神经网络，其架构推广了基于圆形指纹的标准分子特征提取方法，说明这些数据驱动的特征更易解释，并且在多种任务上具有更好的预测性能。