# DESC_MOL-DDIE

**Repository Path**: wangxiaolei21/DESC_MOL-DDIE

## Basic Information

- **Project Name**: DESC_MOL-DDIE
- **Description**: Using drug descriptions and molecular structures for drug–drug interaction extraction from literature
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-11-05
- **Last Updated**: 2021-11-05

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## DESC\_MOL-DDIE
Implementation of Using Drug Description and Molecular Structures for Drug-Drug Interaction Extraction from Literature

### Requirements
torch >= 1.2  
transformers == 2.1  
rdkit (please install via conda ```conda install -c conda-forge rdkit ```)  
lxml  
apex (for argument ```--fp16```, optional)  

### Usage
#### Preparation of the corpus sets
see [corpus/README.md](corpus/README.md)

#### Preparation of the DrugBank data
see [database/README.md](database/README.md)

#### Preparation of the molecular fingerprints data
see [fingerprint/README.md](fingerprint/README.md)

#### Preparation of the SciBERT model
pre-trained SciBERT model is available [here](https://s3-us-west-2.amazonaws.com/ai2-s2-research/scibert/huggingface_pytorch/scibert_scivocab_uncased.tar)

#### Sample data set
When you use the [sample data set](sample) created by splitting the official training data set, you can skip the preparation of the corpus and the database.
```
export NEW_TSV_DIR=sample/tsv
export FINGERPRINT_DIR=sample/radius1
export RADIUS=1
python3 fingerprint/preprocessor.py $NEW_TSV_DIR none $RADIUS $FINGERPRINT_DIR
```
change these paths to absolute paths before running ```run_ddie.py```

#### DDI Extraction
```
cd main
python run_ddie.py \
    --task_name MRPC \
    --model_type bert \
    --data_dir $NEW_TSV_DIR \
    --model_name_or_path $SCIBERT_MODEL \
    --per_gpu_train_batch_size 32 \
    --num_train_epochs 3. \
    --dropout_prob .1 \
    --weight_decay .01 \
    --fp16 \
    --do_train \
    --do_eval \
    --do_lower_case \
    --max_seq_length 128 \
    --use_cnn \
    --conv_window_size 5 \
    --pos_emb_dim 10 \
    --activation gelu \
    --desc_conv_window_size 3 \
    --desc_conv_output_size 20 \
    --molecular_vector_size 50 \
    --gnn_layer_hidden 5 \
    --gnn_layer_output 1 \
    --gnn_mode sum \
    --gnn_activation gelu \
    --fingerprint_dir $FINGERPRINT_DIR \
    --output_dir $OUTPUT_DIR
```
when you use description and molecular structure information, please add ```--use_desc``` and ```--use_mol``` arguments respectively.


### Acknowledgement
This work was supported by JSPS KAKENHI Grant Numbers 17K12741 and 20k11962