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[2508.06199] Benchmarking Pretrained Molecular Embedding Models For Molecular Representation Learning
Summary
This article evaluates 25 pretrained molecular embedding models for molecular representation learning, revealing that most show little improvement over traditional methods.
Why It Matters
The findings challenge the efficacy of current molecular embedding models in chemistry, highlighting the need for rigorous evaluation methods. This research is crucial for improving drug design and molecular property prediction, which have significant implications in pharmaceuticals and biotechnology.
Key Takeaways
- Evaluated 25 molecular embedding models across 25 datasets.
- Most models show negligible improvement over baseline methods.
- Only the CLAMP model significantly outperformed traditional molecular fingerprints.
- Raises concerns about the rigor of existing evaluations in molecular representation.
- Proposes solutions and recommendations for future research.
Computer Science > Machine Learning arXiv:2508.06199 (cs) [Submitted on 8 Aug 2025 (v1), last revised 23 Feb 2026 (this version, v4)] Title:Benchmarking Pretrained Molecular Embedding Models For Molecular Representation Learning Authors:Mateusz Praski, Jakub Adamczyk, Wojciech Czech View a PDF of the paper titled Benchmarking Pretrained Molecular Embedding Models For Molecular Representation Learning, by Mateusz Praski and 2 other authors View PDF HTML (experimental) Abstract: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. Subjects: Machine...
