Bioinformatics Commons^™

Computational Molecular Docking Studies Of Small Molecule Inhibitors With The Sars-Cov-2 Spike Protein Variants: In-Silico Drug Discovery Using Virtual Screening And Drug Repurposing Approaches, Grace Gupta

Computational and Data Sciences (MS) Theses

The pandemic caused by the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2019 has caused a global public health crisis of nearly unprecedented scale. In the years following the outbreak, the scientific community has mobilized to develop several vaccines and treatments. Drug repurposing as a strategy for drug development has produced many of the current therapeutic options. The greatest challenge to designing a therapeutic inhibitor of SARS-CoV-2 is the shifting mutational landscape of the virus as it evolves. In this study, we focus on the spike protein as a target for potential inhibitors. We explore two …

Drug Repurposing Using Gene Expression Data Mining, Yue Qiu

Dissertations, Theses, and Capstone Projects

The conventional drug discovery process that employs the "one disease, one target, one drug'' paradigm is expensive, time-consuming, and has a high rate of failure for multi-genic complex diseases. An alternative approach to drug discovery is to repurpose an existing drug that has been used to treat some medical conditions. Drug repurposing is considered a promising method due to its accelerated the process of drug discovery and lower overall cost and risk.

Drug-perturbed gene expression profiles are powerful phenotype readouts of biological systems, and they have been widely used in drug repurposing studies. However, the existing drug-perturbed gene expression datasets …

A Network-Based Approach For Computational Drug Repurposing On Cancer Data, Ann Reba Thomas Alexander

Electronic Theses and Dissertations

In this thesis, we are interested in finding the best drugs that can be repurposed for the disease and able to find the adverse effects such drugs that are FDA-Approved. Developing an effective drug can be a time-consuming and expensive crucible method. Network-based machine learning methods are used for predicting a given drug for A that can be used for B. It aims at finding new indications for already existing drugs and therefore increases the available therapeutic choices at a fraction of the cost of new drug development. The perturbation gene expression data corresponding to the MCF7 cell line was …

High-Throughput Prediction And Analysis Of Drug-Protein Interactions In The Druggable Human Proteome, Chen Wang

Theses and Dissertations

Drugs exert their (therapeutic) effects via molecular-level interactions with proteins and other biomolecules. Computational prediction of drug-protein interactions plays a significant role in the effort to improve our current and limited knowledge of these interactions. The use of the putative drug-protein interactions could facilitate the discovery of novel applications of drugs, assist in cataloging their targets, and help to explain the details of medicinal efficacy and side-effects of drugs. We investigate current studies related to the computational prediction of drug-protein interactions and categorize them into protein structure-based and similarity-based methods. We evaluate three representative structure-based predictors and develop a Protein-Drug …