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Mslati, Hazem

University of British Columbia

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) caused a global healthcare crisis due to COVID-19 pandemic. In absence of efficacious antiviral drugs against this new Coronavirus species, scientific and clinical efforts initially focused on identifying repositionable FDA-approved drugs to be rapidly deployed worldwide. As the efforts shifted to identifying novel antiviral drugs specific to SARS-CoV-2, while vaccine development took place, computer-assisted drug discovery (CADD) screening strategies, predominantly through molecular docking, emerged as versatile, fast, and economic means to prioritize novel and promising compounds that could progress swiftly to clinical validation. Alternative to docking, are scoring functions (SFs) inferred from machine learning (ML) models. Such models are trained on protein-ligand interactions and can be used to predict binding affinity of novel molecules against a target of interest. The first chapter of this thesis discusses computational methods used throughout this work focused on CADD enabled identification of novel direct acting antivirals (DAA) for SARS-CoV-2 pathogen. The second chapter reconciliates numerous SARS-CoV-2 repurposing studies while underscoring discrepancies, mapping drug ontologies and highlighting repositionable drugs with the highest agreement among the studies. Importantly, we experimentally validate those candidate drugs – reporting still divergent potency values. We conclude with describing a sustainable route of drug repurposing based on the collective findings. The third chapter, on the other hand, focuses on the shift towards de-novo drug discovery through molecular docking of screening compounds against the main protease (Mpro) enzyme of the SARS-CoV-2. Therein, we utilized five different computational protocols and employed a manifold of hit-diversifying filtering strategies on a record-breaking database of 40 billion molecules. The ultra-large screening resulted in the discovery of two promising noncovalent compounds for translational development. Finally, in the fourth chapter of the thesis, we describe a proof of concept of a new SF alternative to molecular docking consisting of a graph neural network (GNN) model with residual skip connections and attention-based graph pooling. We capitalize on the reported results from the third chapter’s for benchmarking the model’s performance. We report a state-of-the-art performance of the proposed model (Pearson > 0.8 across DAVIS, KIBA, BindingDB and PDBBind databases).

Text

2023-02-24

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/83859

Bioinformatics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/