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Chance Factors In Studies Of Quantitative Structure-activity Relationships.

J. G. Topliss, R. Edwards
Published 1979 · Chemistry, Medicine

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Multiple regression analysis is a basic statistical tool used for QSAR studies in drug design. However, there is a risk or arriving at fortuitous correlations when too many variables are screened relative to the number of available observations. In this regard, a critical distinction must be made between the number of variables screened for possible correlation and the number which actually appear in the regression equation. Using a modified Fortran stepwise multiple-regression analysis program, simulated QSAR studies employing random numbers were run for many different combinations of screened variables and observations. Under certain conditions, a substantial incidence of correlations with high r2 values were found, although the overall degree of chance correlation noted was less than that reported in a previous study. Analysis of the results has provided a basis for making judgements concerning the level of risk of encountering chance correlations for a wide range of combinations of observations and screened variables in QSAR studies using multiple-regression analysis. For illustrative purposes, some examples involving published QSAR studies have been considered and the reported correlations shown to be less significant than originally presented through the influence of unrecognized chance factors.
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This paper is referenced by
10.1039/P29920001545
Investigation of a charge-transfer substituent constant using computational chemistry and pattern recognition techniques
D. Livingstone (1992)
10.1201/9780203642627.CH7
Building QSAR Models: A Practical Guide
D. Livingstone (2004)
10.1039/C1MD00135C
Chemometric modeling and pharmacophore mapping in coronary heart disease: 2-arylbenzoxazoles as cholesteryl ester transfer protein inhibitors
Dhritiman Jana (2011)
10.1007/s10822-008-9171-1
Bond-based linear indices in QSAR: computational discovery of novel anti-trichomonal compounds
Y. Marrero-Ponce (2008)
10.1016/0027-5107(94)90029-9
Quantitative structure-activity relationship (QSAR) studies in genetic toxicology: mathematical models and the "biological activity" term of the relationship.
R. Benigni (1994)
10.1016/S0223-5234(98)80023-8
QSAR/QSTR of fluoroquinolones: an example of simultaneous analysis of multiple biological activities using neural network method
Y. Tang (1998)
10.1007/s10822-004-4071-5
Validation tools for variable subset regression
K. Baumann (2004)
10.1016/S1381-1177(01)00022-4
Isolation and purification of cysteine peptidases from the latex of Araujia hortorum fruits: Study of their esterase activities using partial least-squares (PLS) modeling
N. Priolo (2001)
10.1016/j.ejmech.2008.04.020
QSAR analysis of salicylamide isosteres with the use of quantum chemical molecular descriptors.
R. Dolezal (2009)
10.1556/AMICR.50.2003.4.6
QSAR analysis of antibacterial activity of some 4-aminodiphenylsulfone derivatives.
V. Agrawal (2003)
10.1016/0898-5529(90)90120-W
Comparative molecular field analysis (CoMFA). 2. Toward its use with 3D-structural databases
M. Clark (1990)
10.1002/JCC.540120815
A computer automated structure evaluation (CASE) approach to calculation of partition coefficient
G. Klopman (1991)
10.1002/CEM.1180060404
A robust PLS procedure
I. N. Wakelinc (1992)
10.1021/AC00087A012
Prediction of Reduced Ion Mobility Constants from Structural Information Using Multiple Linear Regression Analysis and Computational Neural Networks
M. Wessel (1994)
10.1080/10629369508029913
Synergism between QSAR Modeling and Physico-Chemical Principles
O. Mekenyan (1995)
10.1021/ci000140s
Prediction of C60 Solubilities from Solvent Molecular Structures
S. Danauskas (2001)
10.1002/EM.2860070503
Computer analysis of toxicological data bases: mutagenicity of aromatic amines in Salmonella tester strains.
G. Klopman (1985)
10.1007/978-3-642-77260-3_30
QSAR studies in genetic toxicology: congeneric and non congeneric chemicals.
R. Benigni (1992)
10.1016/J.BEJ.2019.04.013
Evaluation of inhibition of lignocellulose-derived by-products on bioethanol production by using the QSAR method and mechanism study
J. Hou (2019)
10.1016/S0045-6535(02)00508-8
Comparative assessment of methods to develop QSARs for the prediction of the toxicity of phenols to Tetrahymena pyriformis.
M. Cronin (2002)
10.4018/978-1-60566-705-8.ch006
Neural Networks in Medicine
R. Logeswaran (2010)
10.1016/S0169-7439(00)00109-X
Application of nonlinear and local modeling methods for 3D QSAR study of class I antiarrhythmics
A. Borosy (2000)
10.1111/J.1399-3011.1998.TB01247.X
Identification of the motilide pharmacophores using quantitative structure activity relationships.
A. Khiat (1998)
10.1016/S0960-894X(01)81246-4
The use of neural networks for variable selection in QSAR
J. H. Wikel (1993)
10.1016/S0021-9673(01)97719-2
Calculation of retention indices by molecular topology: chlorinated alkanes.
A. Sabljic (1984)
10.1016/j.bmc.2011.09.032
Affinity prediction on A3 adenosine receptor antagonists: the chemometric approach.
F. Luan (2011)
Molecular quantum similarity in QSAR: applications in computer-aided molecular design
A. G. Saliner (2004)
10.1016/J.SUPFLU.2012.10.008
Chemometric analysis of tocopherols content in soybean oil obtained by supercritical CO2
Stela Jokić (2012)
10.2478/s11532-011-0071-1
Investigation of 6-fluoroquinolones activity against Mycobacterium tuberculosis using theoretical molecular descriptors: a case study
Nikola Minovski (2011)
The QSARome of the receptorome: Quantitative structure-activity relationship modeling of multiple ligand sets acting at multiple receptors
G. Zhao (2011)
10.1007/s11030-006-9036-2
SVM approach for predicting LogP
Q. Liao (2006)
10.1021/ci060113n
An Improved Approximation to the Estimation of the Critical F Values in Best Subset Regression
D. Salt (2007)
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