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. The data and code are available here: https://github.com/azminewasi/Drug-Classification-NLP.
Cite
@article{arxiv.2403.12984,
title = {When SMILES have Language: Drug Classification using Text Classification Methods on Drug SMILES Strings},
author = {Azmine Toushik Wasi and Šerbetar Karlo and Raima Islam and Taki Hasan Rafi and Dong-Kyu Chae},
journal= {arXiv preprint arXiv:2403.12984},
year = {2024}
}
Comments
7 pages, 2 figures, 5 tables, Accepted (invited to present) to the The Second Tiny Papers Track at ICLR 2024 (https://openreview.net/forum?id=VUYCyH8fCw)