  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.
