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README.md
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tags:
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- biology
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- chemistry
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library_name: tdc
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license: bsd-2-clause
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---
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## Dataset statistics
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Total: 1,975; Train_val: 1,580; Test: 395
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## Dataset split
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Random split on 70% training, 10% validation, and 20% testing
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To load the dataset in TDC, type
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```python
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from tdc.single_pred import ADME
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data = ADME(name = 'BBB_Martins')
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```
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## Model description
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AttentiveFP is a Graph Attention Network-based molecular representation learning method.
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To load the pre-trained model, type
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```python
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tdc_hf.predict_deeppurpose(dp_model, ['YOUR SMILES STRING'])
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```
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## References
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tags:
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- biology
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- chemistry
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- therapeutic science
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- drug design
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- drug development
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- therapeutics
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library_name: tdc
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license: bsd-2-clause
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---
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## Dataset statistics
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Total: 1,975; Train_val: 1,580; Test: 395
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## Dataset split
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Random split on 70% training, 10% validation, and 20% testing
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To load the dataset in TDC, type
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```python
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from tdc.single_pred import ADME
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data = ADME(name = 'BBB_Martins')
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```
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## Model description
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AttentiveFP is a Graph Attention Network-based molecular representation learning method. The model is tuned with 100 runs using the Ax platform.
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To load the pre-trained model, type
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```python
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tdc_hf.predict_deeppurpose(dp_model, ['YOUR SMILES STRING'])
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```
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## References
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* Dataset entry in Therapeutics Data Commons, https://tdcommons.ai/single_pred_tasks/adme/#bbb-blood-brain-barrier-martins-et-al
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* Martins, Ines Filipa, et al. “A Bayesian approach to in silico blood-brain barrier penetration modeling.” Journal of chemical information and modeling 52.6 (2012): 1686-1697.
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