readme: ---
  pipeline_tag: sentence-similarity
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  ---
  
  # This model is superseded by [https://github.com/ORNL/affinity_pred](https://github.com/ORNL/affinity_pred)
  
  # jglaser/protein-ligand-mlp-3
  
  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).
  
  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.
  
  <!--- Describe your model here -->
  
  ## Usage (Sentence-Transformers)
  
  Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
  
  ```
  #pip install -U sentence-transformers
  pip install git+https://github.com/jglaser/sentence-transformers.git@enable_mixed
  ```
  
  Then you can use the model like this:
  
  ```python
  from sentence_transformers import SentenceTransformer
  sentences = [{'protein': ["SEQVENCE"], 'ligand': ["c1ccccc1"]}]
  
  model = SentenceTransformer('jglaser/protein-ligand-mlp-3')
  embeddings = model.encode(sentences)
  print(embeddings)
  ```
  
  
  ## Evaluation Results
  
  <!--- Describe how your model was evaluated -->
  
  ## Full Model Architecture
  ```
  SentenceTransformer(
    (0): Asym(
      (protein-0): Transformer({'max_seq_length': 2048, 'do_lower_case': False}) with Transformer model: BertModel 
      (protein-1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
      (protein-2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
      (ligand-0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
      (ligand-1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
      (ligand-2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
    )
    (1): Dense({'in_features': 1792, 'out_features': 1000, 'bias': True, 'activation_function': 'torch.nn.modules.activation.GELU'})
    (2): Dense({'in_features': 1000, 'out_features': 1000, 'bias': True, 'activation_function': 'torch.nn.modules.activation.GELU'})
    (3): Dense({'in_features': 1000, 'out_features': 1000, 'bias': True, 'activation_function': 'torch.nn.modules.activation.GELU'})
    (4): Dense({'in_features': 1000, 'out_features': 1, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
    (5): Dense({'in_features': 1, 'out_features': 1, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  )
  ```
  
  ## Citing & Authors
  - [Andrew E Blanchard](https://github.com/blnchrd)
  - [John Gounley](https://github.com/gounley)
  - [Debsindhu Bhowmik](https://github.com/debsindhu)
  - [Mayanka Chandra Shekar](https://github.com/mayankachandrashekar)
  - [Isaac Lyngaas](https://github.com/irlyngaas)
  - Shang Gao
  - Junqi Yin
  - Aristeidis Tsaris
  - Feiyi Wang
  - [Jens Glaser](https://github.com/jglaser)
  
  Find more information in our [bioRxiv preprint](https://www.biorxiv.org/content/10.1101/2021.12.10.471928v1)
  