
# This model is superseded by [https://github.com/ORNL/affinity_pred](https://github.com/ORNL/affinity_pred)

# jglaser/protein-ligand-mlp-2

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-2')
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)

{"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"}
sentence-transformers feature-extraction sentence-similarity endpoints_compatible region:us
TAGS
#sentence-transformers #feature-extraction #sentence-similarity #endpoints_compatible #region-us 


# This model is superseded by URL

# jglaser/protein-ligand-mlp-2

This is a sentence-transformers 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.



## Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:



Then you can use the model like this:




## Evaluation Results



## Full Model Architecture


## Citing & Authors
- Andrew E Blanchard
- John Gounley
- Debsindhu Bhowmik
- Mayanka Chandra Shekar
- Isaac Lyngaas
- Shang Gao
- Junqi Yin
- Aristeidis Tsaris
- Feiyi Wang
- Jens Glaser

Find more information in our bioRxiv preprint

# This model is superseded by URL # jglaser/protein-ligand-mlp-2

This is a sentence-transformers 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. ## Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:



Then you can use the model like this: ## Evaluation Results ## Full Model Architecture ## Citing & Authors
- Andrew E Blanchard
- John Gounley
- Debsindhu Bhowmik
- Mayanka Chandra Shekar
- Isaac Lyngaas
- Shang Gao
- Junqi Yin
- Aristeidis Tsaris
- Feiyi Wang
- Jens Glaser

Find more information in our bioRxiv preprint
TAGS
#sentence-transformers #feature-extraction #sentence-similarity #endpoints_compatible #region-us 
 # This model is superseded by URL # jglaser/protein-ligand-mlp-2

This is a sentence-transformers 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. ## Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:



Then you can use the model like this: ## Evaluation Results ## Full Model Architecture ## Citing & Authors
- Andrew E Blanchard
- John Gounley
- Debsindhu Bhowmik
- Mayanka Chandra Shekar
- Isaac Lyngaas
- Shang Gao
- Junqi Yin
- Aristeidis Tsaris
- Feiyi Wang
- Jens Glaser

Find more information in our bioRxiv preprint
passage: TAGS
#sentence-transformers #feature-extraction #sentence-similarity #endpoints_compatible #region-us 
# This model is superseded by URL# jglaser/protein-ligand-mlp-2

This is a sentence-transformers 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.## Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:



Then you can use the model like this:## Evaluation Results## Full Model Architecture## Citing & Authors
- Andrew E Blanchard
- John Gounley
- Debsindhu Bhowmik
- Mayanka Chandra Shekar
- Isaac Lyngaas
- Shang Gao
- Junqi Yin
- Aristeidis Tsaris
- Feiyi Wang
- Jens Glaser

Find more information in our bioRxiv preprint