| --- |
| license: mit |
| library_name: transformers |
| tags: |
| - chemistry |
| - cheminformatics |
| - natural-products |
| - smiles |
| --- |
| |
| # CLaM-NP |
|
|
| SMILES language models for **natural-product-likeness** scoring (CLaM-NP Score). |
|
|
| This repository hosts up to three causal language models, one per subfolder: |
|
|
| | Subfolder | Training data | Role | |
| |--------------|---------------|-------------------------------| |
| | `natural` | COCONUT | P(x \| natural) | |
| | `synthetic` | ZINC22 | P(x \| synthetic) | |
| | `general` | ChEMBL | P(x \| general) — stabilizer | |
|
|
| Tokenizer: [`kohbanye/SmilesTokenizer_PubChem_1M`](https://huggingface.co/kohbanye/SmilesTokenizer_PubChem_1M). |
|
|
| ## Training data |
|
|
| The three models are trained on a joint scaffold split: the scaffolds of all three |
| datasets are pooled and clustered together (ECFP4 Tanimoto >= 0.6), and each |
| cluster goes to a single split, so no scaffold is shared between the training data |
| of one dataset and the evaluation data of another. The split is built by |
| [`scripts/joint_split.py`](https://github.com/ohuelab/clamnp/blob/main/scripts/joint_split.py). |
|
|
| - `natural`: COCONUT, August 2025 release (`coconut_csv-08-2025.zip`) |
| - `synthetic`: 700,000 compounds drawn at random from ZINC 22. A random draw |
| cannot be repeated, so the sample is deposited here as `data/zinc22_sample.smi` |
| (one SMILES per line, as standardized for training). |
| - `general`: ChEMBL 35 |
|
|
| ## Usage |
|
|
| ```python |
| from clamnp import CLaMNPScorer |
| |
| scorer = CLaMNPScorer.from_pretrained() # this repo |
| print(scorer.score("CC(=O)Oc1ccccc1C(=O)O")) # CLaM-NP Score |
| ``` |
|
|
| Or load a single model directly: |
|
|
| ```python |
| from transformers import AutoModelForCausalLM |
| model = AutoModelForCausalLM.from_pretrained("kohbanye/clamnp", subfolder="natural") |
| ``` |
|
|
