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drug-prot-tokenizer-v2-mistral
Kamyar-zeinalipour/drug-prot-tokenizer-v2-mistral
drug-prot-tokenizer-v2-mistral is a machine learning model from Kamyar-zeinalipour. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
[](https://doi.org/10.1016/j.cmpbup.2026.100257) [](https://github.com/KamyarZeinalipour/PharMistral) [](https://huggingface.co/collections/Kamyar-zeinalipour/pharmistral-cmpb-update-2026-6af48fca521e8481db608782)
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Updated Nov 11, 2026
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At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Dec 25, 2024
- Updated
- Nov 11, 2026
- SHA
- 7b5028ab
Model card
From the Hugging Face model README
drug-prot-tokenizer-v2-mistral
The tokenizer used by the PharMistral models, based on the Mistral-7B SentencePiece vocabulary and applied to both protein sequences and molecule strings.
From PharMistral: Drug generation for novel protein targets by Mistral Large Language Model (Computer Methods and Programs in Biomedicine Update, 2026). Code: PharMistral.
Artifacts from this paper
| Artifact | |
|---|---|
| mistral-7b-drug-prots | Stage I: Mistral-7B after joint pre-training on protein sequences and small molecules |
| mistral-7b-drug-prots_pair | Stage II: the final sequence-conditioned model |
| drug-prot-tokenizer-v2-mistral | Tokenizer used for both stages |
| Protein-drug-v2 | Stage I data: unpaired protein and small-molecule sequences |
| Protein-drug-pair-v2 | Stage II data: high-affinity protein–ligand pairs from ChEMBL-26 |
Citation
@article{bendjeddou2026pharmistral,
title = {PharMistral: Drug generation for novel protein targets by Mistral Large Language Model},
author = {Asma Bendjeddou and Kamyar Zeinalipour and Dalal Bardou and Marco Maggini and Franco Scarselli and Monica Bianchini},
journal = {Computer Methods and Programs in Biomedicine Update},
volume = {10},
pages = {100257},
year = {2026},
doi = {10.1016/j.cmpbup.2026.100257}
}
- License
- apache-2.0
- Created
- Dec 25, 2024
- Updated
- Nov 11, 2026