# Benchmarking Pretrained Molecular Embedding Models For Molecular Representation Learning

arXiv: https://arxiv.org/abs/2508.06199 · HTML: https://mlsanity.com/paper/2508.06199/

## 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 (most cited first, 30 of 31)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [MoleculeNet: A Benchmark for Molecular Machine Learning](https://mlsanity.com/paper/1703.00564/) (2017)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [E(n) Equivariant Graph Neural Networks](https://mlsanity.com/paper/2102.09844/) (2021)
- [ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction](https://mlsanity.com/paper/2010.09885/) (2020)
- [Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development](https://mlsanity.com/paper/2102.09548/) (2021)
- [Pre-training Molecular Graph Representation with 3D Geometry](https://mlsanity.com/paper/2110.07728/) (2021)
- [Large-Scale Chemical Language Representations Capture Molecular Structure and Properties](https://mlsanity.com/paper/2106.09553/) (2021)
- [ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction](https://mlsanity.com/paper/2106.06130/) (2021)
- [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning](https://mlsanity.com/paper/2104.06979/) (2021)
- [Topology Adaptive Graph Convolutional Networks](https://mlsanity.com/paper/1710.10370/) (2017)
- [Molecular representation learning with language models and domain-relevant auxiliary tasks](https://mlsanity.com/paper/2011.13230/) (2020)
- [Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis](https://mlsanity.com/paper/1606.04316/) (2016)
- [Molecule Attention Transformer](https://mlsanity.com/paper/2002.08264/) (2020)
- [Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language](https://mlsanity.com/paper/2303.03363/) (2023)
- [SELFormer: Molecular Representation Learning via SELFIES Language Models](https://mlsanity.com/paper/2304.04662/) (2023)
- [Chemical-Reaction-Aware Molecule Representation Learning](https://mlsanity.com/paper/2109.09888/) (2021)
- [Relative Molecule Self-Attention Transformer](https://mlsanity.com/paper/2110.05841/) (2021)
- [Fragment-based Pretraining and Finetuning on Molecular Graphs](https://mlsanity.com/paper/2310.03274/) (2023)
- [A Bayesian Bradley-Terry model to compare multiple ML algorithms on multiple data sets](https://mlsanity.com/paper/2208.04935/) (2022)
- [Scikit-fingerprints: easy and efficient computation of molecular fingerprints in Python](https://mlsanity.com/paper/2407.13291/) (2024)
- [Strengthening structural baselines for graph classification using Local Topological Profile](https://mlsanity.com/paper/2305.00724/) (2023)
- [Molecular Topological Profile (MOLTOP) -- Simple and Strong Baseline for Molecular Graph Classification](https://mlsanity.com/paper/2407.12136/) (2024)

## Built on by (most cited first, 15 of 15)

- [Tabular foundation models for in-context prediction of molecular properties](https://mlsanity.com/paper/2604.16123/) (2026)
- [Mol-JEPA: A multimodal Joint Embedding Predictive Architecture for Molecules](https://mlsanity.com/paper/2608.22642/) (2026)
- [MolPILE -- large-scale, diverse dataset for molecular representation learning](https://mlsanity.com/paper/2509.18353/) (2025)
- [Hash Collisions in Molecular Fingerprints: Effects on Property Prediction and Bayesian Optimization](https://mlsanity.com/paper/2511.17078/) (2025)
- [Monroe: A Molecular Foundation Model for In-Context Probabilistic Inference](https://mlsanity.com/paper/2608.18982/) (2026)
- [Spectral Analysis of Molecular Features: When Richer Features Do Not Guarantee Better Generalization](https://mlsanity.com/paper/2510.14217/) (2025)
- [Freeze, Diffuse, Decode: Task-Aware Adaptation of Transformer Embeddings for Antimicrobial Peptide Design](https://mlsanity.com/paper/2511.23120/) (2025)
- [Diagnosing Heteroskedasticity and Resolving Multicollinearity Paradoxes in Physicochemical Property Prediction](https://mlsanity.com/paper/2512.24643/) (2025)
- [Contrastive Geometric Learning Unlocks Unified Structure- and Ligand-Based Drug Design](https://mlsanity.com/paper/2601.09693/) (2026)
- [Beyond Learning on Molecules by Weakly Supervising on Molecules](https://mlsanity.com/paper/2602.04696/) (2026)
- [SPADE: Faster Drug Discovery by Learning from Sparse Data](https://mlsanity.com/paper/2605.05370/) (2026)
- [GLACIER: A Multimodal Student-Teacher Foundation Model for Molecular Property Prediction](https://mlsanity.com/paper/2606.11382/) (2026)
- [Scikit-fingerprints: Python library for scikit-learn compatible molecular fingerprints and chemoinformatics](https://mlsanity.com/paper/2608.02027/) (2026)
- [Rigorous Evaluation of Large Language Models for Malaria Drug Discovery: Trade-offs in Performance, Scale, and Resource Utility](https://mlsanity.com/paper/2608.20418/) (2026)
- [Role-Aware Morgan Fingerprints for Reaction Yield Prediction](https://mlsanity.com/paper/2609.22167/) (2026)

## 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.