Table 10.

Techniques, attributes, and characteristics of drug discovery-COVID-19 apps

Authors	The basic objective	Pros	Limitations in study	Security method?	Simulation environments	Dataset and Size of Dataset	Using TL?	Mechanism	Application?
Zhang, Wei [115]	Using fused graph information and CNN, propose a transformer network for predicting DTIs	-High scalability -High generalization ability	-High delay	No	Python	DrugBank dataset (Large dataset)	No	CNN	Drug discovery
Amilpur and Bhukya [116]	Proposing a generative LSTM model that learned the molecular language and produced unique compounds	High predictability -Low delay	-Poor flexibility	No	Keras with Tensorflow	ChEMBL and MOSES (Large dataset)	Yes	LSTM	Drug discovery
Deepthi, Jereesh [117]	Using a CNN model to rank clinically approved antiviral medicines according to their effectiveness against SARS-CoV-2	-The approach has an AUC of 0.8897, prediction accuracy of 0.8571, and a sensitivity of 0.8394	-Low security -High delay	No	DLEVDA with the python	The dataset contains 455 human drug–virus associations between 219 drugs and 34 viruses	No	CNN + XGBoost	COVID-19 drug repurposing
Yang, Bogdan [118]	Providing an in silico DL technique for multi-epitope vaccine prediction and design	-High accuracy	-Low robustness	No	GalaxyRefne	The dataset included 5000 latest known B-cell and 2000 T-cell (Medium size dataset)	No	CNN	Vaccine discovery