README.md

Antiviral Dengue Drugs Dataset Acquisition

This repository contains reproducibility steps for Dengue Virus Inhibitors prediction from natural compounds.

The pipeline processes input biosample data to extract biosample IDs, retrieve corresponding SRA accessions, download SRA data, and perform quality control using Fastp.The downloaded data is followed by running the notebooks in this oeder; 1) preprocessing_01.ipynb2) dimentionality_reduction_2_updated.ipynb 3) Hyperparameter_Tuning_machine_learning_3.ipynb and 4) processing_test_dataset_4.ipynb

python descriptorCalculator.py

Data Wrangling and Preprocessing

scripts/descriptorCalculator.py scripts/molecular_docking.sh scripts/rank_binding_affinity.py scripts/split_sdf.py scripts/toSMI.py notebooks/EDA_1_Wrangling.ipynb notebooks/EDA_2_Visualization.ipynb notebooks/ML_Dengue.ipynb output/Models output/Models/LR_model.pkl

Reproducibility check

Model Assessment and Validation

Compounds Prediction

Envelop protein determination of Ligand binding site

python rank_vina

Virtual Screening and Mechanism of Binding Characterization

ADMET Screening of Selected Compounds

After using OSIRIS DataWarrior, the hits with potential pharmacokinetic and toxicity moieties will be removed.

Calculations of Ligand-Receptor Complexes using MMPBSA

The binding free energies of the protein-ligand complexes and the individual energy contributions of the residues were calculated using the Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) (Kumari et al., 2014). This is a corroboration method of validating the limitation of the current scoring function (Wang et al., 2018). R programming package was used to plot the graphs from the MMPBSA computations.

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