A5005004900- Computational Drug Discovery Methods
- Protein Structure and Dynamics
- Bioinformatics and Genomic Networks
- SARS-CoV-2 and COVID-19 Research
- Machine Learning in Materials Science
- Analytical Chemistry and Chromatography
- Enzyme Structure and Function
- Glycosylation and Glycoproteins Research
- Machine Learning and Data Classification
- Machine Learning and Algorithms
- Legume Nitrogen Fixing Symbiosis
- Topic Modeling
- Peptidase Inhibition and Analysis
- Cystic Fibrosis Research Advances
- Ubiquitin and proteasome pathways
- Healthcare Systems and Challenges
- Systemic Sclerosis and Related Diseases
- Biochemical Acid Research Studies
- Microbial Metabolic Engineering and Bioproduction
- COVID-19 Clinical Research Studies
- Essential Oils and Antimicrobial Activity
- Microbial Natural Products and Biosynthesis
- vaccines and immunoinformatics approaches
- Chemical Synthesis and Analysis
- Eosinophilic Disorders and Syndromes
NHS England
2021
University of Toronto
2013-2015
Canada Research Chairs
2013-2014
Hospital for Sick Children
2013-2014
Cystic Fibrosis (CF) is caused by mutations in the CFTR gene, of which over 2000 have been reported to date. Mutations yet be analyzed aggregate assess their distribution across tertiary structure protein, an approach that could provide valuable insights into structure-function relationship CFTR. In addition, binding site Class I correctors (VX-809, VX-661, and C18) not well understood. this study, exonic mutant allele frequencies described 3 curated databases (ABCMdb, CFTR1, CFTR2,...
DCAF1 functions as a substrate recruitment subunit for the RING-type CRL4DCAF1 and HECT family EDVPDCAF1 E3 ubiquitin ligases. The WDR domain of serves binding platform proteins is also targeted by HIV SIV lentiviral adaptors to induce ubiquitination proteasomal degradation antiviral host factors. It therefore attractive both potential therapeutic target development chemical inhibitors an ligase that could be recruited novel PROTACs protein degradation. In this study, we used proteome-scale...
The COVID-19 pandemic has highlighted the urgent need for identification of new antiviral drug therapies a variety diseases. is caused by infection with human coronavirus SARS-CoV-2, while other related coronaviruses cause diseases ranging from severe respiratory infections to common cold. We developed computational approach identify targets and repurpose clinically-relevant compounds treatment range Our based on graph convolutional networks (GCN) involves multiscale host-virus interactome...
The chemical modification of natural compounds is a promising strategy to improve their frequently poor bioavailability and low potency. This study aimed at synthesizing derivatives carvone, monoterpene with anti-inflammatory properties, which we recently identified, evaluating potential activity. Fourteen carvone were synthesized, purified structures confirmed. Noncytotoxic concentrations the test selected based on resazurin reduction assay. Among tested compounds, four significantly...
Objective Transforming growth factor β1 ( TGF β1) is considered a key in fibrogenesis, and blocking signaling pathways diminishes fibrogenesis animal models. The objective of this study was to determine whether nelfinavir mesylate NFV ), drug approved by the Food Drug Administration FDA ) for treating HIV infection, could be repurposed treat pulmonary fibrosis patients with systemic sclerosis SS c). Methods Normal human lung, ventricular, skin fibroblasts as well lung from c were used...
Abstract Benchmarking the performance of generative methods for drug design is complex and multifaceted. In this report, we propose a separation concerns de novo design, categorizing task into three main categories: generation , discrimination exploration . We demonstrate that changes to any these impacts benchmark tasks. present Deriver, an open‐source Python package acts as modular framework molecule generation, with focus on integrating multiple methods. Using changing parameters related...
There is an immediate need to discover treatments for COVID-19, the pandemic caused by SARS-CoV-2 virus. Standard small molecule drug discovery workflows that start with library screens are impractical path forward given timelines discover, develop, and test clinically. To accelerate time patient testing, here we explored therapeutic potential of drugs have been tested some degree in a clinical environment, including approved medications, as possible interventions COVID-19. Motivating our...
Abstract Background As a response to the acute strain placed on National Health Service during first wave of coronavirus disease 2019 in UK, number junior doctors including ENT trainees were redeployed other clinical specialties. This presented these with novel challenges and opportunities. Methods A qualitative study was performed explore experiences, undertaking semi-structured interviews between 17th 30th July. Participants recruited through purposeful sampling. Interview transcripts...
Performance of neural network models relies on the availability large datasets with minimal levels uncertainty. Transfer Learning (TL) have been proposed to resolve issue small dataset size by letting model train a bigger, task-related reference and then fine-tune smaller, task-specific dataset. In this work, we apply transfer learning approach improve predictive power in noisy data systems variable confidence datasets. We propose deep method called Filtered (FTL) that defines multiple tiers...
Abstract The prediction of absorption, distribution, metabolism, excretion, and toxicity (ADMET) small molecules from their molecular structure is a central problem in medicinal chemistry with great practical importance drug discovery. Creating predictive models conventionally requires substantial trial-and-error for the selection representations, machine learning (ML) algorithms, hyperparameter tuning. A generally applicable method that performs well on all datasets without tuning would be...
Abstract The COVID-19 pandemic has led to an urgent need for the identification of new antiviral drug therapies that can be rapidly deployed treat patients with this disease. is caused by infection human coronavirus SARS-CoV-2. We developed a computational approach identify targets and repurpose clinically-relevant compounds treatment COVID-19. Our based on graph convolutional networks (GCN) involves multiscale host-virus interactome analysis coupled off-target predictions. Cellbased...
Benchmarking the performance of generative methods for drug design is complex and multifaceted. In this report, we propose a separation concerns de novo design, categorizing task into three main categories: generation, discrimination, exploration. We demonstrate that changes to any these impacts benchmark tasks. report present Deriver, an open-source Python package acts as modular framework molecule with focus on integrating multiple methods. Using changing parameters related each chemical...
Benchmarking the performance of generative methods for drug design is complex and multifaceted. In this report, we propose a separation concerns de novo design, categorizing task into three main categories: generation, discrimination, exploration. We demonstrate that changes to any these impacts benchmark tasks. report present Deriver, an open-source Python package acts as modular framework molecule with focus on integrating multiple methods. Using changing parameters related each chemical...
<p> </p> <p>The COVID-19 pandemic has highlighted the urgent need for identification of new antiviral drug therapies a variety diseases. is caused by infection with human coronavirus SARS-CoV-2, while other related coronaviruses cause diseases ranging from severe respiratory infections to common cold. We developed computational approach identify targets and repurpose clinically-relevant compounds treatment range Our based on graph convolutional networks (GCN) involves...
<p> </p> <p>The COVID-19 pandemic has highlighted the urgent need for identification of new antiviral drug therapies a variety diseases. is caused by infection with human coronavirus SARS-CoV-2, while other related coronaviruses cause diseases ranging from severe respiratory infections to common cold. We developed computational approach identify targets and repurpose clinically-relevant compounds treatment range Our based on graph convolutional networks (GCN) involves...
<p>The COVID-19 pandemic has highlighted the urgent need for identification of new antiviral drug therapies a variety diseases. is caused by infection with human coronavirus SARS-CoV-2, while other related coronaviruses cause diseases ranging from severe respiratory infections to common cold. We developed computational approach identify targets and repurpose clinically-relevant compounds treatment range Our based on graph convolutional networks (GCN) involves multiscale host-virus...
<p>The COVID-19 pandemic has highlighted the urgent need for identification of new antiviral drug therapies a variety diseases. is caused by infection with human coronavirus SARS-CoV-2, while other related coronaviruses cause diseases ranging from severe respiratory infections to common cold. We developed computational approach identify targets and repurpose clinically-relevant compounds treatment range Our based on graph convolutional networks (GCN) involves multiscale host-virus...