Special course with Morten Nielsen
Experiments are based on DeepLigand.
Extensive experiments were performed as to residual network structure and LSTM structure. Other conclusions are drawn of minor and more superficial experiments. Note: All experiments were conducted on binding affinity data only due to limited computational power available.
Conclusions drawn :
- 5-layer residual network of 'cabd' structure performs the best
- Evaluating residual network outputs in a Gaussian distribution hurts overall performance.
- Using a fully LSTM structure helps the LSTM.
- ReZero initialization is not helpful
- Learning rate scheduler does not help (superficial experiments only)
| Model | PCC | AUC |
|---|---|---|
| Residual Network | 0.803 | 0.927 |
| Ensemble of Residual Network | 0.812 | 0.931 |
| NetMHCPan 3.0 | 0.799 | 0.933 |
The Residual Network primarily outperforms NetMHCPan 3.0 on outlier alleles, while the ensembl network fairly consistently outperforms NetMHCPan 3.0.
conda create -n MHC_experiments python=3.7
conda activate MHC_experiments
# use the instructions from https://pytorch.org/
conda install pytorch=1.5.1 torchvision cudatoolkit=10.2 -c pytorch
pip install -r requirements.txt
mkdir ./experiments/Example of how to train:
python main.py --seed 20200904 --lstm_nhidden 128 --lstm_nlayers 5 --full_lstm True