https://github.com/lehgtrung/egfr-att

Drug effect prediction using neural network
https://github.com/lehgtrung/egfr-att

attention-mechanism classification cnn drug-discovery egfr

Last synced: 3 months ago
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Drug effect prediction using neural network

• Host: GitHub
• URL: https://github.com/lehgtrung/egfr-att
• Owner: lehgtrung
• Created: 2019-04-07T18:33:12.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2020-09-14T07:55:57.000Z (about 4 years ago)
• Last Synced: 2024-07-16T17:01:04.161Z (4 months ago)
• Topics: attention-mechanism, classification, cnn, drug-discovery, egfr
• Language: Python
• Homepage:
• Size: 18.4 MB
• Stars: 19
• Watchers: 3
• Forks: 6
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-drug-discovery - [Python Reference

README

        
Attention-based Multi-input Neural network

=============

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/attention-based-multi-input-deep-learning/drug-discovery-on-egfr-inh)](https://paperswithcode.com/sota/drug-discovery-on-egfr-inh?p=attention-based-multi-input-deep-learning)



## How to install

Using `conda`:

```bash

conda env create -n egfr -f environment.yml

conda activate egfr

```

## Usage

The working folder is `egfr-att/egfr` for the below instruction.

#### To train with Train/Test scheme, use:

```bash

python single_run.py --mode train

```

The original data will be splitted into training/test parts with ratio 8:2. 

When training completed, to evaluate on test data, use:

```bash

python single_run.py --mode test --model_path 

# For example:

python single_run.py --mode test --model_path data/trained_models/model_TEST_BEST

```

ROC curve plot for test data will be placed in egfr/vis folder.

#### To train with 5-fold cross validation scheme, use:

```bash

python cross_val.py --mode train

``` 

When training completed, to evaluate on test data, use:

```bash

python cross_val.py --mode test --model_path 

# For example:

python cross_val.py --mode test --model_path data/trained_models/model_TEST_BEST

```

ROC curve plot for test data will be placed in `egfr/vis/` folder.

#### Attention weight visualization

To visualized attention weight of the model, use:

```bash

python weight_vis.py --dataset  --modelpath 

# For example:

python weight_vis.py --dataset data/egfr_10_full_ft_pd_lines.json --modelpath data/trained_models/model_TEST_BEST

```

By default, all data will be used to to extract attention weights. However, 

only samples with prediction output over a threshold (0.2) are chosen.

## Citation

Please cite our study:

```

Pham, H.N., & Le, T.H. (2019). Attention-based Multi-Input Deep Learning Architecture for Biological Activity Prediction: An Application in EGFR Inhibitors. ArXiv, abs/1906.05168.

```

Bibtex:

```

@article{Pham2019AttentionbasedMD,

  title={Attention-based Multi-Input Deep Learning Architecture for Biological Activity Prediction: An Application in EGFR Inhibitors},

  author={Huy Ngoc Pham and Trung Hoang Le},

  journal={2019 11th International Conference on Knowledge and Systems Engineering (KSE)},

  year={2019},

  pages={1-9}

}

```