A5027906390- Computational Drug Discovery Methods
- SARS-CoV-2 and COVID-19 Research
- Receptor Mechanisms and Signaling
- Analytical Methods in Pharmaceuticals
- Tuberculosis Research and Epidemiology
- Microbial Natural Products and Biosynthesis
- HER2/EGFR in Cancer Research
- Machine Learning in Materials Science
- Bioinformatics and Genomic Networks
- Fullerene Chemistry and Applications
- vaccines and immunoinformatics approaches
- COVID-19 diagnosis using AI
- Pharmacogenetics and Drug Metabolism
- Chemical Synthesis and Analysis
- Graphene research and applications
- Advanced Chemical Physics Studies
- COVID-19 Clinical Research Studies
The University of Texas at El Paso
2017-2021
University of New Mexico
2020
Strategies for drug discovery and repositioning are urgently need with respect to COVID-19. Here we present REDIAL-2020, a suite of computational models estimating small molecule activities in range SARS-CoV-2-related assays. Models were trained using publicly available, high-throughput screening data by employing different descriptor types various machine learning strategies. describe the development use eleven that span across areas viral entry, replication, live virus infectivity, vitro...
Signaling bias is a feature of many G protein-coupled receptor (GPCR) targeting drugs with potential clinical implications. Whether it therapeutically advantageous for drug to be protein biased or β-arrestin depends on the context signaling pathway. Here, we explored GPCR ligands that exhibit gain insights into scaffolds and pharmacophores lead bias. More specifically, considered BiasDB, database containing information about ligands, focused our analysis which show either Five different...
<p> </p><div> <div> <p>Strategies for drug discovery and repositioning are an urgent need with respect to COVID-19. Here we present "REDIAL-2020", a suite of computational models estimating small molecule activities in range SARS-CoV-2 related assays. Models were trained using publicly available, high throughput screening data by employing different descriptor types various machine learning strategies. describe the development usage eleven spanning across areas...
Abstract Strategies for drug discovery and repositioning are an urgent need with respect to COVID-19. We developed "REDIAL-2020", a suite of machine learning models estimating small molecule activity from molecular structure, range SARS-CoV-2 related assays. Each classifier is based on three distinct types descriptors (fingerprint, physicochemical, pharmacophore) parallel model development. These were trained using high throughput screening data the NCATS COVID19 portal...
Abstract Kinases are one of the most important classes drug targets for therapeutic use. Algorithms that can accurately predict drug-kinase inhibitor constant ( pK i ) kinases considerably accelerate discovery process. In this study, we have developed computational models, leveraging machine learning techniques, to ligand-kinase values. Kinase-ligand K data was retrieved from Drug Target Commons (DTC) and Metz databases. Machine models were based on structural physicochemical features...
Strategies for drug discovery and repositioning are an urgent need with respect to COVID-19. We developed "REDIAL-2020", a suite of machine learning models estimating small molecule activity from molecular structure, range SARS-CoV-2 related assays. Each classifier is based on three distinct types descriptors (fingerprint, physicochemical, pharmacophore) parallel model development. These were trained using high throughput screening data the NCATS COVID19 portal...
Strategies for drug discovery and repositioning are an urgent need with respect to COVID-19. We developed "REDIAL-2020", a suite of machine learning models estimating small molecule activity from molecular structure, range SARS-CoV-2 related assays. Each classifier is based on three distinct types descriptors (fingerprint, physicochemical, pharmacophore) parallel model development. These were trained using high throughput screening data the NCATS COVID19 portal...
Strategies for drug discovery and repositioning are an urgent need with respect to COVID-19. Here we present "REDIAL-2020", a suite of computational models estimating small molecule activities in range SARS-CoV-2 related assays. Models were trained using publicly available, high throughput screening data by employing different descriptor types various machine learning strategies. describe the development usage eleven spanning across areas viral entry, replication, live virus infectivity,...
Signaling bias is a feature of many G–protein coupled receptor (GPCR) modulating drugs with clinical implications. Whether it therapeutically advantageous for drug to be G Protein biased or β -Arrestin ( -Arr) biased, depends on the context signaling pathway. Here, we explored GPCR ligands that exhibit gain insights into scaffolds and pharmacophores leads bias. More specifically, used BiasDB, database containing information about all which show / protein -Arr are considered study. Four...
<p>Signaling bias is a feature of many G–protein coupled receptor (GPCR) modulating drugs with clinical implications. Whether it therapeutically advantageous for drug to be G Protein biased or <i>β</i>-Arrestin (<i>β</i>-Arr) biased, depends on the context signaling pathway. Here, we explored GPCR ligands that exhibit gain insights into scaffolds and pharmacophores leads bias. More specifically, used BiasDB, database containing information about all which show /...
Signaling bias is a feature of many G–protein coupled receptor (GPCR) modulating drugs with clinical implications. Whether it therapeutically advantageous for drug to be G Protein biased or β -Arrestin ( -Arr) biased, depends on the context signaling pathway. Here, we explored GPCR ligands that exhibit gain insights into scaffolds and pharmacophores leads bias. More specifically, used BiasDB, database containing information about all which show / protein -Arr are considered study. Four...