metadata

tags:
  - generated_from_trainer
  - chemistry
  - medical
  - drug_drug_interaction
metrics:
  - f2-score
  - recall
  - precision
  - mcc
model-index:
  - name: Bio_ClinicalBERT_DDI_finetuned
    results:
      - task:
          name: Drug - Drug Interaction Classification
          type: text-classification
        dataset:
          name: DrugBank
          type: REST API
        metrics:
          - name: Recall
            type: recall
            value: 0.7849
widget:
  - text: '[Ca++].[O-]C([O-])=O [SEP] OC[C@H](O)[C@@H](O)[C@H](O)[C@H](O)CO'
    example_title: Drug1 [SEP] Drug2
pipeline_tag: text-classification

Bio_ClinicalBERT_DDI_finetuned

This model was initialized from Bio_ClinicalBERT by adding three hidden layers after the BERT pooler layer. The model was trained on the Drug-Drug Interaction dataset extracted from DrugBank database and National Library of Medicine API. It achieves the following results on the Test dataset:

F2: 0.7872
AUPRC: 0.869
Recall: 0.7849
Precision: 0.7967
MCC: 0.3779

Model description

Predict Drug Drug Interaction (DDI) from Chemical Structure of two drugs. The Model returns the probability of the two drugs having interaction with each other.

Intended uses & limitations

To construct the input, using "[SEP]" token to seperate between the two drugs, example of a properly constructed input is as following

drug1 = "[Ca++].[O-]C([O-])=O"  #Calcium Carbonate
drug2 = "OC[C@H](O)[C@@H](O)[C@H](O)[C@H](O)CO"   #Sorbitol
correct_input = "[Ca++].[O-]C([O-])=O [SEP] OC[C@H](O)[C@@H](O)[C@H](O)[C@H](O)CO"

Training and evaluation data

To avoid data leakage and able to predict DDI for new drugs, the drug1 or drug2 in the validation and the test set were not included in the training set. Their SMILES chemical structures were never exposed to the training process.

Training procedure

Using AWS EC2 g5.4xlarge instance. 24GB GPU.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.01
train_batch_size: 32
eval_batch_size: 32
seed: 7
optimizer: Adadelta with weight_decay=1e-04
lr_scheduler_type: CosineAnnealingLR
num_epochs: 4

Training results

Training Loss	Epoch	Validation Loss	F2	Recall	Precision	Mcc
0.6068	1.0	0.7061	0.6508	0.6444	0.6778	0.2514
0.4529	2.0	0.8334	0.7555	0.7727	0.6939	0.3451
0.3375	3.0	0.9582	0.7636	0.7840	0.6915	0.3474
0.2624	4.0	1.2588	0.7770	0.8004	0.6954	0.3654

Framework versions

Transformers 4.30.2
Pytorch 2.0.1
Datasets 2.13.1
Tokenizers 0.13.3