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Kamyar-zeinalipour/mistral-7b-drug-prots
mistral-7b-drug-prots is a text generation model from Kamyar-zeinalipour. Use it when you need the model to write or continue text. The card lists the license as apache-2.0.
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From the Hugging Face model README
PharMistral · Stage I
</div>Mistral-7B after joint pre-training on unpaired protein sequences and drug-like molecules. This is the intermediate checkpoint; the final sequence-conditioned model is mistral-7b-drug-prots_pair.
From PharMistral: Drug generation for novel protein targets by Mistral Large Language Model (Computer Methods and Programs in Biomedicine Update, 2026). Code: PharMistral.
Stage I of a two-stage process on Mistral-7B: joint pre-training on unpaired protein and small-molecule sequences, then sequence-conditioned fine-tuning on high-affinity protein–ligand pairs from ChEMBL-26. Sequence length 1,500, batch size 4 × 4 gradient-accumulation steps on 4 GPUs, learning rate 5e-5 with cosine decay, fill-in-the-middle rate 0.5. Use the matching tokenizer drug-prot-tokenizer-v2-mistral. Training scripts: GitHub.
| 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 |
Research use only. Generated molecules are computational hypotheses: they have not been synthesised or tested experimentally, and they require the usual medicinal-chemistry, safety and regulatory evaluation before any practical use.
@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}
}