entropy
/

erbb1_mlp

@@ -1,199 +1,81 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+tags:
+- chemistry
+- molecule
+license: mit
 ---
+# Model Card for ErbB1 MLP
 ### Model Description
+`erbb1_mlp` is a MLP-style model trained to predict ErbB1 (EGFR) binding affinity from
+embeddings generated by the [roberta_zinc_480m](https://huggingface.co/entropy/roberta_zinc_480m)
+model.
+- **Developed by:** Karl Heyer
+- **License:** MIT
 ### Direct Use
+Usage examples. Note that input SMILES strings should be canonicalized.
+With the Transformers library:
+```python
+from sentence_transformers import models, SentenceTransformer
+from transformers import AutoModel
+transformer = models.Transformer("entropy/roberta_zinc_480m",
+                                 max_seq_length=256,
+                                 model_args={"add_pooling_layer": False})
+pooling = models.Pooling(transformer.get_word_embedding_dimension(),
+                         pooling_mode="mean")
+roberta_zinc = SentenceTransformer(modules=[transformer, pooling])
+erbb1_mlp = AutoModel.from_pretrained("entropy/erbb1_mlp", trust_remote_code=True)
+# smiles should be canonicalized
+smiles = [
+    "Brc1cc2c(NCc3ccccc3)ncnc2s1",
+    "Brc1cc2c(NCc3ccccn3)ncnc2s1",
+    "Brc1cc2c(NCc3cccs3)ncnc2s1",
+    "Brc1cc2c(NCc3ccncc3)ncnc2s1",
+    "Brc1cc2c(Nc3ccccc3)ncnc2s1"
+]
+embeddings = roberta_zinc.encode(smiles, convert_to_tensor=True)
+predictions = erbb1_mlp(embeddings).predictions
+```
 ### Training Procedure
+#### Preprocessing
+ErbB1 ligands were downloaded from ChEMBL (`target_chembl_id="CHEMBL203"`, `type="IC50"`,
+`relation="="`, `assay_type="B"`). Results were filtered for assays with IC50 values in nM
+for homo sapiens, canonicalized and deduplicated. IC50 values were converted to pIC50 values.
+The final dataset contains 7327 data points.
+Prior to training, pIC50 values were normalized. The model was trained on normalized values,
+and uses the saved mean/variance of the dataset to denormalize predictions.
 #### Training Hyperparameters
+The model was trained for 30 epochs with a batch size of 32, learing rate of 1e-3, weight decay of
+1e-4 and cosine learning rate scheduling.
+## Model Card Authors
+Karl Heyer
 ## Model Card Contact
+[email protected]
+---
+license: mit
+---