MolEncoder
MolEncoder is a BERT-based chemical language model pretrained on SMILES strings using masked language modeling (MLM). It was designed to investigate optimal pretraining strategies for molecular representation learning, with a particular focus on masking ratio, dataset size, and model size. It is described in detail in the paper "MolEncoder: Towards Optimal Masked Language Modeling for Molecules".
Model Description
- Architecture: Encoder-only transformer based on ModernBERT
- Parameters: ~15M
- Tokenizer: Character-level tokenizer covering full SMILES vocabulary
- Pretraining Objective: Masked language modeling with optimized masking ratios (30% found to work best for molecules)
- Pretraining Data: Pretrained on ~1M molecules (half of ChEMBL)
Key Findings
- Higher masking ratios (20–60%) outperform the standard 15% used in prior molecular BERT models.
- Increasing model size or dataset size beyond moderate scales yields no consistent performance benefits and can degrade efficiency.
- This 15M parameter model pretrained on ~1M molecules outperforms much larger models pretrained on more SMILES strings.
Intended Uses
- Primary use: Molecular property prediction through fine-tuning on downstream datasets
How to Use
Please refer to the MolEncoder GitHub repository for detailed instructions and ready-to-use examples on fine-tuning the model on custom data and running predictions.
Citation
If you use this model, please cite:
@Article{D5DD00369E,
author ="Krüger, Fabian P. and Österbacka, Nicklas and Kabeshov, Mikhail and Engkvist, Ola and Tetko, Igor",
title ="MolEncoder: towards optimal masked language modeling for molecules",
journal ="Digital Discovery",
year ="2025",
pages ="-",
publisher ="RSC",
doi ="10.1039/D5DD00369E",
url ="http://dx.doi.org/10.1039/D5DD00369E"}