@article {doi:10.1142/S2737416522500107,
	title = {Deep Sequence Models for Ligand-Based Virtual Screening},
	journal = {Journal of Computational Biophysics and Chemistry},
	volume = {21},
	number = {02},
	year = {2022},
	note = {n/a},
	pages = {207-217},
	abstract = {<p>The past few years have witnessed machine learning techniques take the limelight in multiple research domains. One such domain that has reaped the benefits of machine learning is computer-aided drug discovery, where the search space for candidate drug molecules is decreased using methods such as virtual screening. Current state-of-the-art sequential neural network models have shown promising results and we would like to replicate similar results with virtual screening using the encoded molecular information known as simplified molecular-input line-entry system (SMILES). Our work includes the use of attention-based sequential models {\textemdash} the long short-term memory with attention and an optimized version of the transformer network specifically designed to deal with SMILES (ChemBERTa). We also propose the {\textquotedblleft}Overall Screening Efficacy{\textquotedblright}, an averaging metric that aggregates and encapsulates the model performance over multiple datasets. We found an overall improvement of about 27\% over the benchmark model, which relied on parallelized random forests.</p>
},
	doi = {10.1142/S2737416522500107},
	url = {https://doi.org/10.1142/S2737416522500107},
	author = {Viswajit Vinod Nair,  Jayaraj , and Pradeep, Sonaal Pathlai and Nair, Vaishnavi Sudheer and Pournami, P. N. and Gopakumar,G and P.B. Jayaraj}
}
