mlsanityData to 7 Oct 2026

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

arXiv 2407.12136 ↗First posted 16 Jul 2024cs.LG q-bio.QMcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 40 of its 75 references that are in the map. First posted on arXiv 16 Jul 2024.

Builds on

40 of 75 references.

References · 40

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28 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 1.

7 papers.

  1. 2025Benchmarking Pretrained Molecular Embedding Models For Molecular Representation Learningcited by 15
  2. 2024Scikit-fingerprints: easy and efficient computation of molecular fingerprints in Pythoncited by 9
  3. 2025Molecular Fingerprints Are Strong Models for Peptide Function Predictioncited by 5
  4. 2025Evaluating machine learning models for predicting pesticide toxicity to honey beescited by 2
  5. 2025Towards Rational Pesticide Design with Graph Machine Learning Models for Ecotoxicologycited by 1
  6. 2025Structure Over Signal: A Globalized Approach to Multi-relational GNNs for Stock Predictioncited by 0
  7. 2026Scikit-fingerprints: Python library for scikit-learn compatible molecular fingerprints and chemoinformaticscited by 0