A new computer program for QSAR‐analysis: ARTE‐QSAR

A new computer program has been designed to build and analyze quantitative–structure activity relationship (QSAR) models through regression analysis. The user is provided with a range of regression and validation techniques. The emphasis of the program lies mainly in the validation of QSAR models in chemical applications. ARTE‐QSAR produces an easy interpretable output from which the user can conclude if the obtained model is suitable for prediction and analysis. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2007.

[1]  Nina Nikolova-Jeliazkova,et al.  QSAR Applicability Domain Estimation by Projection of the Training Set in Descriptor Space: A Review , 2005, Alternatives to laboratory animals : ATLA.

[2]  R. Ponec,et al.  Simple analytical method for evaluation of statistical importance of correlations in QSAR studies , 2000 .

[3]  Douglas M. Hawkins,et al.  Assessing Model Fit by Cross-Validation , 2003, J. Chem. Inf. Comput. Sci..

[4]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[5]  M. Karelson,et al.  Quantum-Chemical Descriptors in QSAR/QSPR Studies. , 1996, Chemical reviews.

[6]  Martyn G. Ford,et al.  Unsupervised Forward Selection: A Method for Eliminating Redundant Variables , 2000, J. Chem. Inf. Comput. Sci..

[7]  C. Jun,et al.  Performance of some variable selection methods when multicollinearity is present , 2005 .

[8]  Paola Gramatica,et al.  The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models , 2003 .

[9]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[10]  V. Barnett,et al.  Applied Linear Statistical Models , 1975 .

[11]  Hans Matter,et al.  Computational Medicinal Chemistry for Drug Discovery , 2004 .

[12]  J. Topliss,et al.  Chance factors in studies of quantitative structure-activity relationships. , 1979, Journal of medicinal chemistry.

[13]  Patrick Bultinck,et al.  Computational medicinal chemistry for drug discovery , 2003 .

[14]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[15]  H. Mewes,et al.  Can we estimate the accuracy of ADME-Tox predictions? , 2006, Drug discovery today.

[16]  Marko Grobelnik,et al.  Subspace, Latent Structure and Feature Selection techniques , 2006 .

[17]  Lindsay I. Smith,et al.  A tutorial on Principal Components Analysis , 2002 .