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

• URL: http://arxiv.org/abs/2407.12136v3
• Date: Tue, 23 Jul 2024 17:58:52 GMT
• Title: Molecular Topological Profile (MOLTOP) -- Simple and Strong Baseline for Molecular Graph Classification
• Authors: Jakub Adamczyk, Wojciech Czech,
• Abstract summary: 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 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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