TY  - JOUR
AU  - Khan, Tauseef
AU  - Hussain, Altaf
AU  - Hussain, Tariq
AU  - Lin, Xianxuan
AU  - Sharafian, Amin
AU  - Monirul, Islam Md
AU  - Laila, Umme
PY  - 2025
DA  - 2025/02/19
TI  - Prediction of Coronavirus Inhibitors in Drug Discovery through Deep Learning
JO  - ICCK Transactions on Advanced Computing and Systems
T2  - ICCK Transactions on Advanced Computing and Systems
JF  - ICCK Transactions on Advanced Computing and Systems
VL  - 1
IS  - 1
SP  - 19
EP  - 31
DO  - 10.62762/TACS.2024.974479
UR  - https://www.icck.org/article/abs/TACS.2024.974479
KW  - drug discovery
KW  - Covid-19
KW  - deep learning
KW  - machine learning
KW  - bio-informatics
AB  - In the therapy of Coronavirus, the drug target is a demanding task to find novel medicine. A bunch of pharmaceutics procedures are employed to recognize these mutual actions. But they are exhausting and high-priced. Keeping this in view, computational procedures are widely approached to determine the mutual action of the medicine and their respective proteins. Many scientists have applied ML approaches to deduce attributes from simplified molecular-input line systems (for medicine) and protein sequences. Such approaches dropped the proteins' chemical, physical, and structural characteristics and the respective medicine. Our job is to undertake deep learning approaches to detect coronavirus enzyme correspondence with the validated Chembl database medicine. The representation of the molecular structure of proteins, medically known as fingerprints, will be done scientifically. Then, a deep learning model will be given training on the pulled-out fingerprints and the properties of molecules to determine the interplay of the medicine with the respective catalyst. The suggested approach will be proficient in recognizing the catalyst's interactivity with the approved database medicine.
SN  - 3068-7969
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Khan2025Prediction,
  author = {Tauseef Khan and Altaf Hussain and Tariq Hussain and Xianxuan Lin and Amin Sharafian and Islam Md Monirul and Umme Laila},
  title = {Prediction of Coronavirus Inhibitors in Drug Discovery through Deep Learning},
  journal = {ICCK Transactions on Advanced Computing and Systems},
  year = {2025},
  volume = {1},
  number = {1},
  pages = {19-31},
  doi = {10.62762/TACS.2024.974479},
  url = {https://www.icck.org/article/abs/TACS.2024.974479},
  abstract = {In the therapy of Coronavirus, the drug target is a demanding task to find novel medicine. A bunch of pharmaceutics procedures are employed to recognize these mutual actions. But they are exhausting and high-priced. Keeping this in view, computational procedures are widely approached to determine the mutual action of the medicine and their respective proteins. Many scientists have applied ML approaches to deduce attributes from simplified molecular-input line systems (for medicine) and protein sequences. Such approaches dropped the proteins' chemical, physical, and structural characteristics and the respective medicine. Our job is to undertake deep learning approaches to detect coronavirus enzyme correspondence with the validated Chembl database medicine. The representation of the molecular structure of proteins, medically known as fingerprints, will be done scientifically. Then, a deep learning model will be given training on the pulled-out fingerprints and the properties of molecules to determine the interplay of the medicine with the respective catalyst. The suggested approach will be proficient in recognizing the catalyst's interactivity with the approved database medicine.},
  keywords = {drug discovery, Covid-19, deep learning, machine learning, bio-informatics},
  issn = {3068-7969},
  publisher = {Institute of Central Computation and Knowledge}
}