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--- |
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library_name: transformers |
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tags: |
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- chemistry |
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- molecule |
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license: mit |
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--- |
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# Model Card for ErbB1 MLP |
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### Model Description |
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`erbb1_mlp` is a MLP-style model trained to predict ErbB1 (EGFR) binding affinity from |
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embeddings generated by the [roberta_zinc_480m](https://huggingface.co/entropy/roberta_zinc_480m) |
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model. |
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- **Developed by:** Karl Heyer |
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- **License:** MIT |
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### Direct Use |
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Usage examples. Note that input SMILES strings should be canonicalized. |
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With the Transformers library: |
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```python |
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from sentence_transformers import models, SentenceTransformer |
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from transformers import AutoModel |
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transformer = models.Transformer("entropy/roberta_zinc_480m", |
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max_seq_length=256, |
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model_args={"add_pooling_layer": False}) |
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pooling = models.Pooling(transformer.get_word_embedding_dimension(), |
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pooling_mode="mean") |
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roberta_zinc = SentenceTransformer(modules=[transformer, pooling]) |
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erbb1_mlp = AutoModel.from_pretrained("entropy/erbb1_mlp", trust_remote_code=True) |
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# smiles should be canonicalized |
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smiles = [ |
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"Brc1cc2c(NCc3ccccc3)ncnc2s1", |
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"Brc1cc2c(NCc3ccccn3)ncnc2s1", |
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"Brc1cc2c(NCc3cccs3)ncnc2s1", |
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"Brc1cc2c(NCc3ccncc3)ncnc2s1", |
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"Brc1cc2c(Nc3ccccc3)ncnc2s1" |
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] |
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embeddings = roberta_zinc.encode(smiles, convert_to_tensor=True) |
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predictions = erbb1_mlp(embeddings).predictions |
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``` |
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### Training Procedure |
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#### Preprocessing |
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ErbB1 ligands were downloaded from ChEMBL (`target_chembl_id="CHEMBL203"`, `type="IC50"`, |
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`relation="="`, `assay_type="B"`). Results were filtered for assays with IC50 values in nM |
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for homo sapiens, canonicalized and deduplicated. IC50 values were converted to pIC50 values. |
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The final dataset contains 7327 data points. |
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Prior to training, pIC50 values were normalized. The model was trained on normalized values, |
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and uses the saved mean/variance of the dataset to denormalize predictions. |
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#### Training Hyperparameters |
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The model was trained for 30 epochs with a batch size of 32, learing rate of 1e-3, weight decay of |
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1e-4 and cosine learning rate scheduling. |
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## Model Card Authors |
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Karl Heyer |
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## Model Card Contact |
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karl@darmatterai.xyz |
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--- |
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license: mit |
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--- |
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