---
license: mit
metrics:
- accuracy
- f1
- precision
pipeline_tag: text-classification
tags:
- medical
- chemistry
- biology
---
# ***When SMILES have Language*: Drug Classification using Text Classification Methods on Drug SMILES Strings**
- **Authors:** Azmine Toushik Wasi, Šerbetar Karlo, Raima Islam, Taki Hasan Rafi, Dong-Kyu Chae
- Accepted (***invited to present***) to the **The Second Tiny Papers Track at ICLR 2024**!
- Read full paper in [arXiv](https://arxiv.org/abs/2403.12984).
---

<p align="center">
  <img src="Fig/model.png" width="1000"/>
</p>

**Abstract**: Complex chemical structures, like drugs, are usually defined by SMILES strings as a sequence of molecules and bonds. These SMILES strings are used in different complex machine learning-based drug-related research and representation works. Escaping from complex representation, in this work, we pose a single question: What if we treat drug SMILES as conventional sentences and engage in text classification for drug classification? Our experiments affirm the possibility with very competitive scores. The study explores the notion of viewing each atom and bond as sentence components, employing basic NLP methods to categorize drug types, proving that complex problems can also be solved with simpler perspectives.

---

# Setup and run
- Data is available at `./Model/_DATA_`
- Dataloader is available at `./Model/data`
- To run the training script, place the dataset from DrugBank, go to `./Model/` folder and run: `python train-ngram.py`
- To change parameters, you can check and edit `145-165` no lines of `./Model/train-ngram.py`

# Experimental Results

| Model          | Accuracy | Precision | Recall | F1 (Weighted) | F1 (Macro) | ROC-AUC |
|----------------|----------|-----------|--------|----------------|-------------|---------|
| 1-gram+MLP     | 0.622    | 0.610     | 0.622  | 0.604          | 0.406       | 0.760   |
| 2-gram+MLP     | 0.669    | 0.700     | 0.669  | 0.672          | 0.445       | 0.810   |
| 3-gram+MLP     | **0.737**| **0.764** | **0.737**| **0.744**     | 0.553       | **0.848**|
| 4-gram+MLP     | 0.726    | 0.758     | 0.726  | 0.731          | 0.524       | 0.841   |
| 5-gram+MLP     | 0.728    | 0.740     | 0.728  | 0.730          | **0.563**   | 0.838   |
| AtomPair+MLP   | 0.799    | 0.804     | 0.800  | 0.799          | 0.702       | 0.876   |
| MACCS+MLP      | 0.797    | 0.801     | 0.797  | 0.796          | 0.702       | 0.873   |
| Morgan+MLP     | **0.800**| **0.804** | **0.800**| **0.799**     | **0.703**   | **0.876**|
|




# Citation
```
@inproceedings{wasi2024drug_nlp,,
  author       = {Azmine Toushik Wasi and Šerbetar Karlo and Raima Islam and Taki Hasan Rafi and Dong-Kyu Chae},
  title        = {When SMILES have Language: Drug Classification using Text Classification Methods on Drug SMILES Strings},
  booktitle    = {The Second Tiny Papers Track at {ICLR} 2024, Tiny Papers @ {ICLR} 2024, Vienna Austria, May 11, 2024},
  publisher    = {OpenReview.net},
  year         = {2023},
  url          = {https://openreview.net/forum?id=VUYCyH8fCw}
}
```