Implementation of IFPTML Computational Models in Drug Discovery Against Flaviviridae Family

Abstract

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.

Figure 1

General workflow.

Figure 2

AUCROC curve graph, the diagonal line represents random probability is 0.5, In the graph, it can be observed how the values of probability changes, orange represents the validation set and blue the training set.

Figure 3

LGBM Classification Report of the IFPTML-Flaviviridae D_k30 model—validation data set.

Figure 4

Calibration plots of the IFPTML-Flaviviridae D_k30—LGBM.

Figure 5

LGBM confusion matrix using the validation data set of Flaviviridae D_k30.

Figure 6

Feature Importance Plot of the IFPTML-Flaviviridae D_k30-LGBM model.

Figure 7

AUCROC curve for the LGBM classifier of IFPTML-Flaviviridae D_k30 model.