Three-Dimensional Quantitative Structure− Activity Relationships from Molecular Similarity Matrices and Genetic Neural Networks. 2. Applications
SS So, M Karplus - Journal of Medicinal Chemistry, 1997 - ACS Publications
Validation of a method that uses a genetic neural network with electrostatic and steric
similarity matrices (SM/GNN) to obtain quantitative structure− activity relationships (QSARs) …
similarity matrices (SM/GNN) to obtain quantitative structure− activity relationships (QSARs) …
External validation and prediction employing the predictive squared correlation coefficient Test set activity mean vs training set activity mean
G Schuurmann, RU Ebert, J Chen… - Journal of chemical …, 2008 - ACS Publications
The external prediction capability of quantitative structure− activity relationship (QSAR)
models is often quantified using the predictive squared correlation coefficient, q 2. This index …
models is often quantified using the predictive squared correlation coefficient, q 2. This index …
First report of q-RASAR modeling toward an approach of easy interpretability and efficient transferability
A Banerjee, K Roy - Molecular Diversity, 2022 - Springer
Quantitative structure–activity relationship (QSAR) and read-across techniques have
recently been merged into a new emerging field of read-across structure–activity …
recently been merged into a new emerging field of read-across structure–activity …
alvaDesc: A tool to calculate and analyze molecular descriptors and fingerprints
A Mauri - Ecotoxicological QSARs, 2020 - Springer
In this chapter we will present alvaDesc, a software to calculate and analyze molecular
descriptors and fingerprints. Molecular descriptors and fingerprints play an essential role in …
descriptors and fingerprints. Molecular descriptors and fingerprints play an essential role in …
An automated framework for QSAR model building
S Kausar, AO Falcao - Journal of cheminformatics, 2018 - Springer
Background In-silico quantitative structure–activity relationship (QSAR) models based tools
are widely used to screen huge databases of compounds in order to determine the …
are widely used to screen huge databases of compounds in order to determine the …
Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information
Abstract The Online Chemical Modeling Environment is a web-based platform that aims to
automate and simplify the typical steps required for QSAR modeling. The platform consists of …
automate and simplify the typical steps required for QSAR modeling. The platform consists of …
Some case studies on application of “rm2” metrics for judging quality of quantitative structure–activity relationship predictions: Emphasis on scaling of response …
Quantitative structure–activity relationship (QSAR) techniques have found wide application
in the fields of drug design, property modeling, and toxicity prediction of untested chemicals …
in the fields of drug design, property modeling, and toxicity prediction of untested chemicals …
PyDescriptor: A new PyMOL plugin for calculating thousands of easily understandable molecular descriptors
VH Masand, V Rastija - Chemometrics and Intelligent Laboratory Systems, 2017 - Elsevier
Abstract The field of Quantitative Structure-Activity Relationship (QSAR) relies heavily on
molecular descriptors. Among various guidelines suggested by Organisation for Economic …
molecular descriptors. Among various guidelines suggested by Organisation for Economic …
A new semi-automated workflow for chemical data retrieval and quality checking for modeling applications
D Gadaleta, A Lombardo, C Toma… - Journal of …, 2018 - Springer
The quality of data used for QSAR model derivation is extremely important as it strongly
affects the final robustness and predictive power of the model. Ambiguous or wrong …
affects the final robustness and predictive power of the model. Ambiguous or wrong …