MTGL-ADMET: A Novel Multi-Task Graph Learning Framework for ADMET Prediction Enhanced by Status-Theory and Maximum Flow

A Multi-Task Graph Learning framework for predicting multiple ADMET properties of drug-like small molecules (MTGL-ADMET) under a new paradigm of MTL, "one primary, multiple auxiliaries".

Step-by-step running:

1. Requirements

• Python == 3.8
• dgl == 0.4.3
• scikit-learn == 0.24.2
• pandas == 1.2.0
• numpy == 1.20.2
• rdkit

2. Create data

Obtain graph data

Running

python create_graph_data.py

3. Running

python Training.py

Here, we used ‘CYP2C9’ as the example. As for other ADMET endpoints, the model can be adjusted according to the auxiliary tasks.

Acknowledgements

Part of the code was adopted from [1].

References

[1] Wu Z, Jiang D, Wang J, et al. Mining toxicity information from large amounts of toxicity data[J]. Journal of Medicinal Chemistry, 2021, 64(10): 6924-6936.