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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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An integrated Ether-a-go-go-related gene (hERG) dataset consisting of molecular structures labeled as hERG (<10uM) and non-hERG (>=10uM) blockers in the form of SMILES strings was obtained from the DeepHIT, the BindingDB database, ChEMBL bioactivity database, and other literature. |
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## Task description |
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Binary classification. Given a drug SMILES string, predict whether it blocks (1, <10uM) or not blocks (0, >=10uM). |
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## Dataset statistics |
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Total: 13445; Train_val: 12620; Test: 825 |
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## Pre-requisites |
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Install the following packages |
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``` |
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pip install PyTDC |
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pip install DeepPurpose |
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pip install git+https://github.com/bp-kelley/descriptastorus |
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pip install dgl torch torchvision |
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``` |
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You can also reference the colab notebook [here](https://colab.research.google.com/drive/1CL92SOCBS-eYDL99w8tjSNIG_ySXzMrG?usp=sharing) |
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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 Tox |
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data = Tox(name = 'herg_karim') |
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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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To load the pre-trained model, type |
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```python |
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from tdc import tdc_hf_interface |
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tdc_hf = tdc_hf_interface("hERG_Karim-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, ['CC(=O)NC1=CC=C(O)C=C1']) |
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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/tox |
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* Karim, A., et al. CardioTox net: a robust predictor for hERG channel blockade based on deep learning meta-feature ensembles. J Cheminform 13, 60 (2021). https://doi.org/10.1186/s13321-021-00541-z |
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