A comprehensive and detailed essay about Y-Scrambling/Y-Randomization.

(A)

Y-Randomization – A Useful Tool in QSAR Validation, or Folklore? - Christoph Rücker, Gerta Rücker, and Markus Meringer

(B)

J Chem Inf Model. 2007 Nov-Dec;47(6):2345-57. Epub 2007 Sep 20.

y-Randomization and its variants in QSPR/QSAR.

Rücker C, Rücker G, Meringer M.

Source

Biozentrum, University of Basel, 4056 Basel, Switzerland. christoph.ruecker@uni-bayreuth.de

Cheminformatics

Cheminformatics for development of new materials (by Ricardo Stefani - in portuguese)

Quimioinformatica

View more presentations from Ricardo Stefani

comp.ai.neural-nets FAQ, Part 1 of 7: Introduction

Neural Networks FAQ

comp.ai.neural-nets FAQ, Part 1 of 7: Introduction

The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models - Tropsha - 2003 - QSAR & Combinatorial Science - Wiley Online Library

The Importance of Being Earnest:

Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models

Alexander Tropsha1,†, Paola Gramatica2, Vijay K. Gombar3
Article first published online: 16 APR 2003

Abstract

This paper emphasizes the importance of rigorous validation as a crucial, integral component of Quantitative Structure Property Relationship (QSPR) model development. We consider some examples of published QSPR models, which in spite of their high fitted accuracy for the training sets and apparent mechanistic appeal, fail rigorous validation tests, and, thus, may lack practical utility as reliable screening tools. We present a set of simple guidelines for developing validated and predictive QSPR models. To this end, we discuss several validation strategies including (1) randomization of the modelled property, also called Y-scrambling, (2) multiple leave-many-out cross-validations, and (3) external validation using rational division of a dataset into training and test sets. We also highlight the need to establish the domain of model applicability in the chemical space to flag molecules for which predictions may be unreliable, and discuss some algorithms that can be used for this purpose. We advocate the broad use of these guidelines in the development of predictive QSPR models.

DOI: 10.1002/qsar.200390007
Copyright © 2003 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Extreme Learning Machine Survey

A Risk Assessment Perspective of Current Practice in Characterizing Uncertainties in QSAR Regression Predictions - Sahlin - 2011 - Molecular Informatics - Wiley Online Library

A Risk Assessment Perspective of Current Practice in Characterizing Uncertainties in QSAR Regression Predictions - Sahlin - 2011 - Molecular Informatics - Wiley Online Library

Best Practices for QSAR Modelling

Interesting papers about the validation and predictivity of QSAR Models:

Validation and Predictivity of QSAR Models - Hugo Kubinyi (lecture slides)

Best Practices for QSAR Model Development, Validation, and Exploitation - Alexander Tropsha - (2010), Best Practices for QSAR Model Development, Validation, and Exploitation. Molecular Informatics, 29: 476–488. doi: 10.1002/minf.201000061

Which MATLAB functions benefit from multithreaded computation?

http://www.mathworks.com/support/solutions/en/data/1-4PG4AN/?solution=1-4PG4AN