[HTML][HTML] Computational methods in drug discovery
SP Leelananda, S Lindert - Beilstein journal of organic …, 2016 - beilstein-journals.org
The process for drug discovery and development is challenging, time consuming and
expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut …
expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut …
Recent advances in ligand-based drug design: relevance and utility of the conformationally sampled pharmacophore approach
In the absence of three-dimensional (3D) structures of potential drug targets, ligand-based
drug design is one of the popular approaches for drug discovery and lead optimization. 3D …
drug design is one of the popular approaches for drug discovery and lead optimization. 3D …
Pitfalls in QSAR
MTD Cronin, TW Schultz - Journal of Molecular Structure: THEOCHEM, 2003 - Elsevier
There are no formal guidelines for the development of quantitative structure–activity
relationships (QSARs). However, there are a number of practices that should be avoided …
relationships (QSARs). However, there are a number of practices that should be avoided …
Improved quantitative structure–activity relationship models to predict antioxidant activity of flavonoids in chemical, enzymatic, and cellular systems
AI Khlebnikov, IA Schepetkin, NG Domina… - Bioorganic & medicinal …, 2007 - Elsevier
Quantitative structure–activity relationship (QSAR) models are useful in understanding how
chemical structure relates to the biological activity of natural and synthetic chemicals and for …
chemical structure relates to the biological activity of natural and synthetic chemicals and for …
[HTML][HTML] Artificial neural networks for computer-based molecular design
G Schneider, P Wrede - Progress in biophysics and molecular biology, 1998 - Elsevier
The theory of artificial neural networks is briefly reviewed focusing on supervised and
unsupervised techniques which have great impact on current chemical applications. An …
unsupervised techniques which have great impact on current chemical applications. An …
Molecular dynamics simulation in drug discovery: opportunities and challenges
R Shukla, T Tripathi - Innovations and implementations of computer aided …, 2021 - Springer
Drug discovery is the process used to discover new candidate medications. In the past, most
drugs were discovered by identification of active-ingredients or by serendipity. Modern drug …
drugs were discovered by identification of active-ingredients or by serendipity. Modern drug …
Quantitative structure− activity relationships of ruthenium catalysts for olefin metathesis
G Occhipinti, HR Bjørsvik… - Journal of the American …, 2006 - ACS Publications
A quantitative structure− activity relationship (QSAR) model is presented in which both the
independent and dependent (response) variables are derived from density functional theory …
independent and dependent (response) variables are derived from density functional theory …
Predicting ligand binding to proteins by affinity fingerprinting
LM Kauvar, DL Higgins, HO Villar, JR Sportsman… - Chemistry & biology, 1995 - Elsevier
Background: There are many ways to represent a molecule's properties, including atomic-
connectivity drawings. NMR spectra, and molecular orbital models. Prior methods for …
connectivity drawings. NMR spectra, and molecular orbital models. Prior methods for …
Applications of quantitative structure-activity relationships (QSAR) based virtual screening in drug design: A review
PGR Achary - Mini Reviews in Medicinal Chemistry, 2020 - ingentaconnect.com
The scientists, and the researchers around the globe generate tremendous amount of
information everyday; for instance, so far more than 74 million molecules are registered in …
information everyday; for instance, so far more than 74 million molecules are registered in …
Method, system and computer program product for non-linear mapping of multi-dimensional data
DK Agrafiotis, VS Lobanov, FR Salemme - US Patent 6,571,227, 2003 - Google Patents
A method, system and computer program product for scaling, or dimensionally reducing,
multi-dimensional data sets, that scales well for large data sets. The invention scales multi …
multi-dimensional data sets, that scales well for large data sets. The invention scales multi …