Comparison of the inhibition of Escherichia coli and Lactobacillus casei dihydrofolate reductase by 2,4-diamino-5-(substituted-benzyl)pyrimidines: quantitative structure-activity relationships, x-ray crystallography, and computer graphics in structure-activity analysis

Hansch, Corwin; Li, Renli; Blaney, Jeffrey M.; Langridge, Robert

doi:10.1021/jm00349a003

Cited by 87 publications

(45 citation statements)

References 2 publications

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“…The QSAR methods studied in this paper have been assessed using fivefold cross-'calidation, in which the 55 molecules (numbers 1-55) from the machine learning study [8] were randomly a Numbers 1-44 have been analysed by linear regression [14]; numbers 45 55 are from Roth and co-workers [17,18]; numbers 2-44 and 56-74 were used in a more recent linear regression study [13] and in a neural network analysis [3]. Six of the 25 have not been included, because the complete set of Hansch parameters for the substituents was not available at the time of this study.…”

Section: Datamentioning

confidence: 99%

Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by pyrimidines

Hirst

King

Sternberg

1994

J Computer-Aided Mol Des

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Neural networks and inductive logic programming (ILP) have been compared to linear regression for modelling the QSAR of the inhibition of E. coli dihydrofolate reductase (DHFR) by 2,4-diamino-5-(substituted benzyl)pyrimidines, and, in the subsequent paper [Hirst, J.D., King, R.D. and Sternberg, M.J.E. J. Comput.-Aided Mol. Design, 8 (1994) 421], the inhibition of rodent DHFR by 2,4-diamino-6,6-dimethyl-5-phenyl-dihydrotriazines. Cross-validation trials provide a statistically rigorous assessment of the predictive capabilities of the methods, with training and testing data selected randomly and all the methods developed using identical training data. For the ILP analysis, molecules are represented by attributes other than Hansch parameters. Neural networks and ILP perform better than linear regression using the attribute representation, but the difference is not statistically significant. The major benefit from the ILP analysis is the formulation of understandable rules relating the activity of the inhibitors to their chemical structure.

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Section: Datamentioning

confidence: 99%

Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by pyrimidines

Hirst

King

Sternberg

1994

J Computer-Aided Mol Des

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“…The program GOLEM was used to model the inhibition of trimethoprim analogues on Escherichia coli (E. coli) dihydrofolate reductase (DHFR) [21]. A training set of 44 compounds were used and the resultant algorithm tested on 11 further compounds [22].…”

Section: D Ilp-based Sarmentioning

confidence: 99%

Structure Activity Relationships (SAR) and Pharmacophore Discovery Using Inductive Logic Programming (ILP)

Sternberg¹,

Muggleton²

2003

QSAR Comb. Sci.

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The application of Inductive Logic Programming (ILP), a form of machine learning, to derive structure activity relationships (SAR) and to discover pharmacophores is reported. The ILP approach was initially applied to model 1D SARs in terms of the attributes of the molecules. Subsequently 2D ILP SARs were developed describing chemical connectivity. Finally ILP has been used to model 3D SARs in which the conformation of the pharmacophore can be described. ILP has advantages over many other widely used methods as it can reason with relations and hence discover chemical substructures and 3D features without these aspects having been explicitly encoded prior to learning. In particular, there is no requirement for a structural superposition. Additionally, the results of ILP provide chemical descriptions that can readily be understood by a medicinal chemist. In several trials, ILP-based SARs have been shown to be significantly more accurate than widely-used methods.

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“…The factors that determine the permeability of cell membranes to drugs are of interest to medicinal chemists and biochemists (1)(2)(3)(4)(5)(6)(7).…”

Section: Introductionmentioning

confidence: 99%

New method for the determination of permeation coefficients of functional chemical groups using membrane model vesicles

Kraus¹,

Micheau²,

Pernice³

et al. 1989

Can. J. Chem.

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. Can. J. Chem. 67, 1606 (1989). New substituent constants IT,, derived from permeation coefficients, of membrane model vesicles have been evaluated for fifteen functional groups. The excellent correlation found between the new substituent constants and the values derived from partition coefficients in octanol-water system leads one to consider this new parameter and the technique used for its evaluation as new promising tools for quantitative structure-activity relationship studies and for hydrophobic properties measurement.

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Comparison of the inhibition of Escherichia coli and Lactobacillus casei dihydrofolate reductase by 2,4-diamino-5-(substituted-benzyl)pyrimidines: quantitative structure-activity relationships, x-ray crystallography, and computer graphics in structure-activity analysis

Cited by 87 publications

References 2 publications

Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by pyrimidines

Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by pyrimidines

Structure Activity Relationships (SAR) and Pharmacophore Discovery Using Inductive Logic Programming (ILP)

New method for the determination of permeation coefficients of functional chemical groups using membrane model vesicles

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