Molecular Topological Profile (MOLTOP) -- Simple and Strong Baseline for Molecular Graph Classification

TL;DR

This paper introduces MOLTOP, a simple, fast, and hyperparameter-free topological descriptor-based method for molecular graph classification that rivals modern GNNs across multiple benchmarks.

Contribution

MOLTOP is a novel, effective baseline combining topological descriptors with a Random Forest, demonstrating competitive performance against advanced GNNs.

Findings

MOLTOP surpasses the 1-WL test in several classes.

It performs comparably to or better than some GNNs.

The method is fast, low-variance, and hyperparameter-free.

Abstract

We revisit the effectiveness of topological descriptors for molecular graph classification and design a simple, yet strong baseline. We demonstrate that a simple approach to feature engineering - employing histogram aggregation of edge descriptors and one-hot encoding for atomic numbers and bond types - when combined with a Random Forest classifier, can establish a strong baseline for Graph Neural Networks (GNNs). The novel algorithm, Molecular Topological Profile (MOLTOP), integrates Edge Betweenness Centrality, Adjusted Rand Index and SCAN Structural Similarity score. This approach proves to be remarkably competitive when compared to modern GNNs, while also being simple, fast, low-variance and hyperparameter-free. Our approach is rigorously tested on MoleculeNet datasets using fair evaluation protocol provided by Open Graph Benchmark. We additionally show out-of-domain generation capabilities on peptide classification task from Long Range Graph Benchmark. The evaluations across eleven benchmark datasets reveal MOLTOP's strong discriminative capabilities, surpassing the $1$-WL test and even $3$-WL test for some classes of graphs. Our conclusion is that descriptor-based baselines, such as the one we propose, are still crucial for accurately assessing advancements in the GNN domain.