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--- |
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language: |
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- en |
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metrics: |
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- accuracy |
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- AUC ROC |
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- precision |
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- recall |
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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 description |
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As a membrane separating circulating blood and brain extracellular fluid, the blood-brain barrier (BBB) is the protective layer that blocks most foreign drugs. Thus the ability of a drug to penetrate the barrier to deliver to the site of action forms a crucial challenge in developing drugs for central nervous system. |
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## Task description |
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Binary classification. Given a drug SMILES string, predict the activity of BBB. |
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## Dataset statistics |
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Total: 1,975 drugs |
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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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Morgan chemical fingerprint with an MLP decoder. The model is tuned with 100 runs using the Ax platform. |
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```python |
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from tdc import tdc_hf_interface |
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tdc_hf = tdc_hf_interface("BBB_Martins-Morgan") |
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# load deeppurpose model from this repo |
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dp_model = tdc_hf.load_deeppurpose('./data') |
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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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