Implementation of IFPTML Computational Models in Drug Discovery Against Flaviviridae Family

The Flaviviridae family consists of single-stranded positive-sense
RNA viruses, which contains the genera Flavivirus, Hepacivirus, Pegivirus, and Pestivirus. Currently, there
is an outbreak of viral diseases caused by this family affecting millions
of people worldwide, leading to significant morbidity and mortality
rates. Advances in computational chemistry have greatly facilitated
the discovery of novel drugs and treatments for diseases associated
with this family. Chemoinformatic techniques, such as the perturbation
theory machine learning method, have played a crucial role in developing
new approaches based on ML models that can effectively aid drug discovery.
The IFPTML models have shown its capability to handle, classify, and
process large data sets with high specificity. The results obtained
from different models indicates that this methodology is proficient
in processing the data, resulting in a reduction of the false positive
rate by 4.25%, along with an accuracy of 83% and reliability of 92%.
These values suggest that the model can serve as a computational tool
in assisting drug discovery efforts and the development of new treatments
against Flaviviridae family diseases.