Be aware of error measures. Further studies on validation of predictive QSAR models

K Roy, RN Das, P Ambure, RB Aher - Chemometrics and Intelligent …, 2016 - Elsevier
Validation is the most crucial concept for development and application of quantitative
structure–activity relationship (QSAR) models. The validation process confirms the reliability …

How important is to detect systematic error in predictions and understand statistical applicability domain of QSAR models?

K Roy, P Ambure, RB Aher - Chemometrics and Intelligent Laboratory …, 2017 - Elsevier
One of the important applications of quantitative structure-activity relationship (QSAR)
models is to fill data gaps by predicting a given response property or activity from known …

Quantum chemical descriptors in quantitative structure–activity relationship models and their applications

L Wang, J Ding, L Pan, D Cao, H Jiang… - … and Intelligent Laboratory …, 2021 - Elsevier
With the accumulation of chemical and biological experimental data and the continuous
development of mathematical statistical algorithms, quantitative structure–activity …

QSAR− how good is it in practice? Comparison of descriptor sets on an unbiased cross section of corporate data sets

P Gedeck, B Rohde, C Bartels - Journal of chemical information …, 2006 - ACS Publications
The quality of QSAR (Quantitative Structure− Activity Relationships) predictions depends on
a large number of factors including the descriptor set, the statistical method, and the data …

Promises and pitfalls of Quantitative Structure− Activity Relationship approaches for predicting metabolism and toxicity

E Zvinavashe, AJ Murk… - Chemical research in …, 2008 - ACS Publications
The description of quantitative structure− activity relationship (QSAR) models has been a
topic for scientific research for more than 40 years and a topic within the regulatory …

Assessing the reliability of a QSAR model's predictions

L He, PC Jurs - Journal of Molecular Graphics and Modelling, 2005 - Elsevier
Quantitative structure activity relationships (QSAR) are one of the well-developed areas in
computational chemistry. In this field, many successful predictive models have been …

[HTML][HTML] Benchmarks for interpretation of QSAR models

M Matveieva, P Polishchuk - Journal of cheminformatics, 2021 - Springer
Abstract Interpretation of QSAR models is useful to understand the complex nature of
biological or physicochemical processes, guide structural optimization or perform …

AutoQSAR: an automated machine learning tool for best-practice quantitative structure–activity relationship modeling

SL Dixon, J Duan, E Smith, CD Von Bargen… - Future medicinal …, 2016 - Taylor & Francis
Aim: We introduce AutoQSAR, an automated machine-learning application to build, validate
and deploy quantitative structure–activity relationship (QSAR) models. Methodology/results …

On some novel similarity-based functions used in the ML-based q-RASAR approach for efficient quantitative predictions of selected toxicity end points

A Banerjee, K Roy - Chemical Research in Toxicology, 2023 - ACS Publications
The novel quantitative read-across structure–activity relationship (q-RASAR) approach uses
read-across-derived similarity functions in the quantitative structure–activity relationship …

[HTML][HTML] OPERA models for predicting physicochemical properties and environmental fate endpoints

K Mansouri, CM Grulke, RS Judson… - Journal of …, 2018 - Springer
The collection of chemical structure information and associated experimental data for
quantitative structure–activity/property relationship (QSAR/QSPR) modeling is facilitated by …