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README.md
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- biology
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---
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Chemma-2B is a continually pretrained [gemma-2b](https://huggingface.co/google/gemma-2b) model for organic molecules.
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It is pretrained on
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(molecular weight, synthetic accessibility score, drug-likeness etc.)
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and similarities (Tanimoto distance between ECFP fingerprints).
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`</s>[SAS]2.25[/SAS][SIMILAR]0.62 CC(=O)OC1=CC=CC=C1C(=O)O[/SIMILAR][START_SMILES]` will attempt to generate a molecule that has 2.25 SAS score and
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has a 0.62 similarity score to the given molecule.
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The model can be wrapped into an optimization loop to traverse the chemical space with evolving prompts.
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A preprint with the details of the model and an optimization algorithm built on top of this model that sets state-of-the-art on
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and other benchmarks
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Few notes:
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* All queries should start with `</s>` symbol.
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- biology
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---
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Chemma-2B is a continually pretrained [gemma-2b](https://huggingface.co/google/gemma-2b) model for organic molecules.
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It is pretrained on [40B tokens covering 110M+ molecules from PubChem](https://huggingface.co/datasets/yerevann/PubChemForLM) as well as their chemical properties
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(molecular weight, synthetic accessibility score, drug-likeness etc.)
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and similarities (Tanimoto distance between ECFP fingerprints).
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`</s>[SAS]2.25[/SAS][SIMILAR]0.62 CC(=O)OC1=CC=CC=C1C(=O)O[/SIMILAR][START_SMILES]` will attempt to generate a molecule that has 2.25 SAS score and
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has a 0.62 similarity score to the given molecule.
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The model can be wrapped into an optimization loop to traverse the chemical space with evolving prompts. See the [code on GitHub](https://github.com/YerevaNN/ChemLactica).
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A preprint with the details of the model and an optimization algorithm built on top of this model that sets state-of-the-art on
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Practical Molecular Optimization and other benchmarks is [available on arxiv](https://arxiv.org/abs/2407.18897).
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Few notes:
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* All queries should start with `</s>` symbol.
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