Big data and artificial intelligence modeling for drug discovery
H Zhu - Annual review of pharmacology and toxicology, 2020 - annualreviews.org
Due to the massive data sets available for drug candidates, modern drug discovery has
advanced to the big data era. Central to this shift is the development of artificial intelligence …
advanced to the big data era. Central to this shift is the development of artificial intelligence …
Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling
Highlights•Drug discovery has been advanced to a big data era with a large amount of
public data sources available.•Ten V features (volume, velocity, variety, veracity, validity …
public data sources available.•Ten V features (volume, velocity, variety, veracity, validity …
Further exploring rm2 metrics for validation of QSPR models
Quantitative structure–property relationship (QSPR) models are widely used for prediction of
properties, activities and/or toxicities of new chemicals. Validation strategies check the …
properties, activities and/or toxicities of new chemicals. Validation strategies check the …
On various metrics used for validation of predictive QSAR models with applications in virtual screening and focused library design
K Roy, I Mitra - Combinatorial chemistry & high throughput …, 2011 - ingentaconnect.com
Quantitative structure-activity relationships (QSARs) have important applications in drug
discovery research, environmental fate modeling, property prediction, etc. Validation has …
discovery research, environmental fate modeling, property prediction, etc. Validation has …
Chemometrics tools in QSAR/QSPR studies: A historical perspective
One of the most extended subfields of chemometrics, at least by considering the number of
publications and interested researchers, is QSAR/QSPR. During the time, various improved …
publications and interested researchers, is QSAR/QSPR. During the time, various improved …
Current approaches for choosing feature selection and learning algorithms in quantitative structure–activity relationships (QSAR)
PM Khan, K Roy - Expert opinion on drug discovery, 2018 - Taylor & Francis
Introduction: Quantitative structure-activity/property relationships (QSAR/QSPR) are
statistical models which quantitatively correlate quantitative chemical structure information …
statistical models which quantitatively correlate quantitative chemical structure information …
Quantitative structure–activity relationship: promising advances in drug discovery platforms
T Wang, MB Wu, JP Lin, LR Yang - Expert opinion on drug …, 2015 - Taylor & Francis
Introduction: Quantitative structure–activity relationship (QSAR) modeling is one of the most
popular computer-aided tools employed in medicinal chemistry for drug discovery and lead …
popular computer-aided tools employed in medicinal chemistry for drug discovery and lead …
[HTML][HTML] Design of novel anti-cancer agents targeting COX-2 inhibitors based on computational studies
The overexpression of cyclooxygenase-2 (COX-2) was clearly associated with
carcinogenesis, and COX-2 as a possible target has long been exploited for cancer therapy …
carcinogenesis, and COX-2 as a possible target has long been exploited for cancer therapy …
Computational prediction of cytochrome P450 inhibition and induction
H Kato - Drug metabolism and pharmacokinetics, 2020 - Elsevier
Cytochrome P450 (CYP) enzymes play an important role in the phase I metabolism of many
xenobiotics. Most drug–drug interactions (DDIs) associated with CYP are caused by either …
xenobiotics. Most drug–drug interactions (DDIs) associated with CYP are caused by either …
Design of novel anti-cancer drugs targeting TRKs inhibitors based 3D QSAR, molecular docking and molecular dynamics simulation
Tropomyosin receptor kinase (TRK) enzymes are responsible for different types of tumors
caused by neurotrophic tyrosine receptor kinase gene fusion and have been identified as an …
caused by neurotrophic tyrosine receptor kinase gene fusion and have been identified as an …