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--- |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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--- |
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# This model is superseded by [https://github.com/ORNL/affinity_pred](https://github.com/ORNL/affinity_pred) |
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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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## Usage (Sentence-Transformers) |
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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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print(embeddings) |
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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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(0): Asym( |
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(protein-0): Transformer({'max_seq_length': 2048, 'do_lower_case': False}) with Transformer model: BertModel |
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(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}) |
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(protein-2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) |
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(ligand-0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel |
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(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}) |
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(ligand-2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) |
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) |
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(1): Dense({'in_features': 1792, 'out_features': 1000, 'bias': True, 'activation_function': 'torch.nn.modules.activation.GELU'}) |
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(2): Dense({'in_features': 1000, 'out_features': 1000, 'bias': True, 'activation_function': 'torch.nn.modules.activation.GELU'}) |
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(3): Dense({'in_features': 1000, 'out_features': 1000, 'bias': True, 'activation_function': 'torch.nn.modules.activation.GELU'}) |
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(4): Dense({'in_features': 1000, 'out_features': 1, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'}) |
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(5): Dense({'in_features': 1, 'out_features': 1, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'}) |
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) |
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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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Find more information in our [bioRxiv preprint](https://www.biorxiv.org/content/10.1101/2021.12.10.471928v1) |
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