mlsanityData to 14 Oct 2026

Benchmarking Pretrained Molecular Embedding Models For Molecular Representation Learning

arXiv 2508.06199 ↗First posted 8 Aug 2025cs.LG cs.AIcited by 15

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Abstract (arXiv)

Pretrained neural networks have attracted significant interest in chemistry and small molecule drug design. Embeddings from these models are widely used for molecular property prediction, virtual screening, and small data learning in molecular chemistry. This study presents the most extensive comparison of such models to date, evaluating 25 models across 25 datasets. Under a fair comparison framework, we assess models spanning various modalities, architectures, and pretraining strategies. Using a dedicated hierarchical Bayesian statistical testing model, we arrive at a surprising result: nearly all neural models show negligible or no improvement over the baseline ECFP molecular fingerprint. Only the CLAMP model, which is also based on molecular fingerprints, performs statistically significantly better than the alternatives. These findings raise concerns about the evaluation rigor in existing studies. We discuss potential causes, propose solutions, and offer practical recommendations.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 31 of its 60 references that are in the map. First posted on arXiv 8 Aug 2025.

Builds on

31 of 60 references.

References · 31

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
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  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  5. 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,703
  6. 2018How Powerful are Graph Neural Networks?cited by 4,393
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  8. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  9. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,051
  10. 2019Strategies for Pre-training Graph Neural Networkscited by 971
  11. 2021E(n) Equivariant Graph Neural Networkscited by 965
  12. 2020ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Predictioncited by 337
19 more
  1. 2021Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Developmentcited by 264
  2. 2021Pre-training Molecular Graph Representation with 3D Geometrycited by 248
  3. 2021Large-Scale Chemical Language Representations Capture Molecular Structure and Propertiescited by 206
  4. 2021ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Predictioncited by 204
  5. 2021TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learningcited by 116
  6. 2017Topology Adaptive Graph Convolutional Networkscited by 113
  7. 2020Molecular representation learning with language models and domain-relevant auxiliary taskscited by 94
  8. 2016Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysiscited by 93
  9. 2020Molecule Attention Transformercited by 92
  10. 2023Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Languagecited by 44
  11. 2023SELFormer: Molecular Representation Learning via SELFIES Language Modelscited by 40
  12. 2021Chemical-Reaction-Aware Molecule Representation Learningcited by 36
  13. 2021Relative Molecule Self-Attention Transformercited by 21
  14. 2023Fragment-based Pretraining and Finetuning on Molecular Graphscited by 20
  15. 2022A Bayesian Bradley-Terry model to compare multiple ML algorithms on multiple data setscited by 9
  16. 2024Scikit-fingerprints: easy and efficient computation of molecular fingerprints in Pythoncited by 9
  17. 2023Strengthening structural baselines for graph classification using Local Topological Profilecited by 7
  18. 2024Molecular Topological Profile (MOLTOP) -- Simple and Strong Baseline for Molecular Graph Classificationcited by 7
  19. 2025Molecular Fingerprints Are Strong Models for Peptide Function Predictioncited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 10.

15 papers.

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  1. 2026Tabular foundation models for in-context prediction of molecular propertiescited by 6
  2. 2026Mol-JEPA: A multimodal Joint Embedding Predictive Architecture for Moleculescited by 2
  3. 2025MolPILE -- large-scale, diverse dataset for molecular representation learningcited by 1
  4. 2025Hash Collisions in Molecular Fingerprints: Effects on Property Prediction and Bayesian Optimizationcited by 1
  5. 2026Monroe: A Molecular Foundation Model for In-Context Probabilistic Inferencecited by 1
  6. 2025Spectral Analysis of Molecular Features: When Richer Features Do Not Guarantee Better Generalizationcited by 0
  7. 2025Freeze, Diffuse, Decode: Task-Aware Adaptation of Transformer Embeddings for Antimicrobial Peptide Designcited by 0
  8. 2025Diagnosing Heteroskedasticity and Resolving Multicollinearity Paradoxes in Physicochemical Property Predictioncited by 0
  9. 2026Contrastive Geometric Learning Unlocks Unified Structure- and Ligand-Based Drug Designcited by 0
  10. 2026Beyond Learning on Molecules by Weakly Supervising on Moleculescited by 0
  11. 2026SPADE: Faster Drug Discovery by Learning from Sparse Datacited by 0
  12. 2026GLACIER: A Multimodal Student-Teacher Foundation Model for Molecular Property Predictioncited by 0
3 more
  1. 2026Scikit-fingerprints: Python library for scikit-learn compatible molecular fingerprints and chemoinformaticscited by 0
  2. 2026Rigorous Evaluation of Large Language Models for Malaria Drug Discovery: Trade-offs in Performance, Scale, and Resource Utilitycited by 0
  3. 2026Role-Aware Morgan Fingerprints for Reaction Yield Predictioncited by 0