Predictive QSAR modeling: methods and applications in drug discovery and chemical risk assessment
Quantitative structure-activity relationship (QSAR) modeling is themajor cheminformatics
approach to exploring and exploiting the dependency of chemical, biological, toxicological …
approach to exploring and exploiting the dependency of chemical, biological, toxicological …
Advances in QSAR modeling
K Roy - … in pharmaceutical, chemical, food, agricultural and …, 2017 - Springer
Quantitative structure–activity/property relationship (QSAR/QSPR) models correlate
biological activity (therapeutic or toxic) or other properties of chemicals/pharmaceuticals …
biological activity (therapeutic or toxic) or other properties of chemicals/pharmaceuticals …
Predictive QSAR modeling workflow, model applicability domains, and virtual screening
A Tropsha, A Golbraikh - Current pharmaceutical design, 2007 - ingentaconnect.com
Quantitative Structure Activity Relationship (QSAR) modeling has been traditionally applied
as an evaluative approach, ie, with the focus on developing retrospective and explanatory …
as an evaluative approach, ie, with the focus on developing retrospective and explanatory …
Application of predictive QSAR models to database mining
A Tropsha - Chemoinformatics in Drug Discovery, 2005 - Wiley Online Library
The field of Quantitative Structure-Activity Relationships (QSAR), as an integral part of
computer-aided drug design and discovery, is experiencing one of the most exciting periods …
computer-aided drug design and discovery, is experiencing one of the most exciting periods …
QSAR modeling: where have you been? Where are you going to?
A Cherkasov, EN Muratov, D Fourches… - Journal of medicinal …, 2014 - ACS Publications
Quantitative structure–activity relationship modeling is one of the major computational tools
employed in medicinal chemistry. However, throughout its entire history it has drawn both …
employed in medicinal chemistry. However, throughout its entire history it has drawn both …
Best practices for QSAR model development, validation, and exploitation
A Tropsha - Molecular informatics, 2010 - Wiley Online Library
After nearly five decades “in the making”, QSAR modeling has established itself as one of
the major computational molecular modeling methodologies. As any mature research …
the major computational molecular modeling methodologies. As any mature research …
Recent Trends in Quantitative Structure‐Activity Relationships
A Tropsha - Burger's Medicinal Chemistry and Drug Discovery, 2003 - Wiley Online Library
The field of quantitative structure‐activity relationships (QSARs) as an integral part of
computer‐aided drug design and discovery is experiencing one of the most exciting periods …
computer‐aided drug design and discovery is experiencing one of the most exciting periods …
QSAR without borders
EN Muratov, J Bajorath, RP Sheridan… - Chemical Society …, 2020 - pubs.rsc.org
Prediction of chemical bioactivity and physical properties has been one of the most
important applications of statistical and more recently, machine learning and artificial …
important applications of statistical and more recently, machine learning and artificial …
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 …
A structural feature-based computational approach for toxicology predictions
LG Valerio Jr, C Yang, KB Arvidson… - Expert opinion on drug …, 2010 - Taylor & Francis
Importance of the field: Evaluation of pharmaceutical-related toxicities using quantitative
structure–activity relationship (QSAR) software as decision support tools is becoming …
structure–activity relationship (QSAR) software as decision support tools is becoming …