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  # jglaser/protein-ligand-mlp-3
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- This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
 
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  <!--- Describe your model here -->
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  Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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  ```
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- pip install -U sentence-transformers
 
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  ```
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  Then you can use the model like this:
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  ```python
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  from sentence_transformers import SentenceTransformer
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- sentences = ["This is an example sentence", "Each sentence is converted"]
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  model = SentenceTransformer('jglaser/protein-ligand-mlp-3')
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  embeddings = model.encode(sentences)
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  ```
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-
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  ## Evaluation Results
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  <!--- Describe how your model was evaluated -->
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- For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=jglaser/protein-ligand-mlp-3)
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-
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-
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-
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  ## Full Model Architecture
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  ```
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  SentenceTransformer(
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  ```
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  ## Citing & Authors
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-
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- <!--- Describe where people can find more information -->
 
 
 
 
 
 
 
 
 
 
 
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  # jglaser/protein-ligand-mlp-3
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+ This is a [sentence-transformers](https://www.SBERT.net) model: It maps pairs of protein and chemical sequences (canonical SMILES) onto binding affinities (pIC50 values).
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+ Each member of the ensemble has been trained using a different seed and you can use the different models as independent samples to estimate the uncertainty.
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  <!--- Describe your model here -->
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  Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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  ```
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+ #pip install -U sentence-transformers
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+ pip install git+https://github.com/jglaser/sentence-transformers.git@enable_mixed
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  ```
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  Then you can use the model like this:
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  ```python
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  from sentence_transformers import SentenceTransformer
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+ sentences = [{'protein': ["SEQVENCE"], 'ligand': ["c1ccccc1"]}]
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  model = SentenceTransformer('jglaser/protein-ligand-mlp-3')
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  embeddings = model.encode(sentences)
 
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  ```
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  ## Evaluation Results
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  <!--- Describe how your model was evaluated -->
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  ## Full Model Architecture
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  ```
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  SentenceTransformer(
 
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  ```
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  ## Citing & Authors
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+ - [Andrew E Blanchard](https://github.com/blnchrd)
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+ - [John Gounley](https://github.com/gounley)
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+ - [Debsindhu Bhowmik](https://github.com/debsindhu)
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+ - [Mayanka Chandra Shekar](https://github.com/mayankachandrashekar)
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+ - [Isaac Lyngaas](https://github.com/irlyngaas)
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+ - Shang Gao
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+ - Junqi Yin
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+ - Aristeidis Tsaris
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+ - Feiyi Wang
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+ - [Jens Glaser](https://github.com/jglaser)
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+
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+ Find more information in our [bioRxiv preprint](https://www.biorxiv.org/content/10.1101/2021.12.10.471928v1)