text:
Computational structure-activity relationship analysis of non-peptide inducers of macrophage tumor necrosis factor-α production
Andrey Ivanovich Khlebnikov, Igor A. Schepetkin, Liliya N. Kirpotina, Mark T. Quinn
Previously, we screened a series of arylcarboxylic acid hydrazide derivatives for their ability to induce macrophage tumor necrosis factor α (TNF-α) production and identified 16 such compounds. In the present study, we evaluated 23 additional arylcarboxylic acid hydrazides and found that seven of these compounds also induced macrophage TNF-α production, representing novel compounds with this activity. The total set of active compounds was then used for computational structure-activity relationship (SAR) analysis to further optimize lead molecules. A sequence of (1) linear discriminant analysis, (2) classification tree analysis with linear combination, and (3) univariate splits based on atom pair descriptors led to the derivation of SAR rule-based algorithms with fitting accuracy of 96.5%, 91.9%, and 84.9%, respectively. The SAR rules obtained from classification tree analysis with univariate splits, which was based on three atom pair descriptors only, revealed that the main factors influencing agonist activity of arylcarboxylic acid hydrazide derivatives were the presence of a methyl or trifluoromethyl group in the benzene ring attached to the furan moiety, an alkoxy group in the aromatic ring near the methylenehydrazide linker, and two or more halogen atoms (chlorine or bromine) on one side of the dumbbell-shaped hydrazide molecule opposed by an aromatic moiety on the opposite side of the molecule. Thus, these rules represent a relatively simple classification approach for de novo design of small-molecule inducers of macrophage TNF-α production.
https://doi.org/10.1016/j.bmc.2008.08.078
Published - 15 Oct 2008
Structure-Activity Relationship
Atom pairs
Molecular descriptors
Structure-activity relationship analysis
Tumor necrosis factor α
Khlebnikov, A. I., Schepetkin, I. A., Kirpotina, L. N., & Quinn, M. T. (2008). Computational structure-activity relationship analysis of non-peptide inducers of macrophage tumor necrosis factor-α production. Bioorganic and Medicinal Chemistry, 16(20), 9302-9312. https://doi.org/10.1016/j.bmc.2008.08.078
Computational structure-activity relationship analysis of non-peptide inducers of macrophage tumor necrosis factor-α production. / Khlebnikov, Andrey Ivanovich; Schepetkin, Igor A.; Kirpotina, Liliya N.; Quinn, Mark T.
In: Bioorganic and Medicinal Chemistry, Vol. 16, No. 20, 15.10.2008, p. 9302-9312.
Khlebnikov, AI, Schepetkin, IA, Kirpotina, LN & Quinn, MT 2008, 'Computational structure-activity relationship analysis of non-peptide inducers of macrophage tumor necrosis factor-α production', Bioorganic and Medicinal Chemistry, vol. 16, no. 20, pp. 9302-9312. https://doi.org/10.1016/j.bmc.2008.08.078
Khlebnikov AI, Schepetkin IA, Kirpotina LN, Quinn MT. Computational structure-activity relationship analysis of non-peptide inducers of macrophage tumor necrosis factor-α production. Bioorganic and Medicinal Chemistry. 2008 Oct 15;16(20):9302-9312. https://doi.org/10.1016/j.bmc.2008.08.078
Khlebnikov, Andrey Ivanovich ; Schepetkin, Igor A. ; Kirpotina, Liliya N. ; Quinn, Mark T. / Computational structure-activity relationship analysis of non-peptide inducers of macrophage tumor necrosis factor-α production. In: Bioorganic and Medicinal Chemistry. 2008 ; Vol. 16, No. 20. pp. 9302-9312.
@article{94cc5159c88e49afaae1b34f9d2bc041,
title = "Computational structure-activity relationship analysis of non-peptide inducers of macrophage tumor necrosis factor-α production",
abstract = "Previously, we screened a series of arylcarboxylic acid hydrazide derivatives for their ability to induce macrophage tumor necrosis factor α (TNF-α) production and identified 16 such compounds. In the present study, we evaluated 23 additional arylcarboxylic acid hydrazides and found that seven of these compounds also induced macrophage TNF-α production, representing novel compounds with this activity. The total set of active compounds was then used for computational structure-activity relationship (SAR) analysis to further optimize lead molecules. A sequence of (1) linear discriminant analysis, (2) classification tree analysis with linear combination, and (3) univariate splits based on atom pair descriptors led to the derivation of SAR rule-based algorithms with fitting accuracy of 96.5{\%}, 91.9{\%}, and 84.9{\%}, respectively. The SAR rules obtained from classification tree analysis with univariate splits, which was based on three atom pair descriptors only, revealed that the main factors influencing agonist activity of arylcarboxylic acid hydrazide derivatives were the presence of a methyl or trifluoromethyl group in the benzene ring attached to the furan moiety, an alkoxy group in the aromatic ring near the methylenehydrazide linker, and two or more halogen atoms (chlorine or bromine) on one side of the dumbbell-shaped hydrazide molecule opposed by an aromatic moiety on the opposite side of the molecule. Thus, these rules represent a relatively simple classification approach for de novo design of small-molecule inducers of macrophage TNF-α production.",
keywords = "Atom pairs, Macrophage, Molecular descriptors, Structure-activity relationship analysis, Tumor necrosis factor α",
author = "Khlebnikov, {Andrey Ivanovich} and Schepetkin, {Igor A.} and Kirpotina, {Liliya N.} and Quinn, {Mark T.}",
doi = "10.1016/j.bmc.2008.08.078",
journal = "Bioorganic and Medicinal Chemistry",
T1 - Computational structure-activity relationship analysis of non-peptide inducers of macrophage tumor necrosis factor-α production
AU - Khlebnikov, Andrey Ivanovich
AU - Schepetkin, Igor A.
AU - Kirpotina, Liliya N.
AU - Quinn, Mark T.
N2 - Previously, we screened a series of arylcarboxylic acid hydrazide derivatives for their ability to induce macrophage tumor necrosis factor α (TNF-α) production and identified 16 such compounds. In the present study, we evaluated 23 additional arylcarboxylic acid hydrazides and found that seven of these compounds also induced macrophage TNF-α production, representing novel compounds with this activity. The total set of active compounds was then used for computational structure-activity relationship (SAR) analysis to further optimize lead molecules. A sequence of (1) linear discriminant analysis, (2) classification tree analysis with linear combination, and (3) univariate splits based on atom pair descriptors led to the derivation of SAR rule-based algorithms with fitting accuracy of 96.5%, 91.9%, and 84.9%, respectively. The SAR rules obtained from classification tree analysis with univariate splits, which was based on three atom pair descriptors only, revealed that the main factors influencing agonist activity of arylcarboxylic acid hydrazide derivatives were the presence of a methyl or trifluoromethyl group in the benzene ring attached to the furan moiety, an alkoxy group in the aromatic ring near the methylenehydrazide linker, and two or more halogen atoms (chlorine or bromine) on one side of the dumbbell-shaped hydrazide molecule opposed by an aromatic moiety on the opposite side of the molecule. Thus, these rules represent a relatively simple classification approach for de novo design of small-molecule inducers of macrophage TNF-α production.
AB - Previously, we screened a series of arylcarboxylic acid hydrazide derivatives for their ability to induce macrophage tumor necrosis factor α (TNF-α) production and identified 16 such compounds. In the present study, we evaluated 23 additional arylcarboxylic acid hydrazides and found that seven of these compounds also induced macrophage TNF-α production, representing novel compounds with this activity. The total set of active compounds was then used for computational structure-activity relationship (SAR) analysis to further optimize lead molecules. A sequence of (1) linear discriminant analysis, (2) classification tree analysis with linear combination, and (3) univariate splits based on atom pair descriptors led to the derivation of SAR rule-based algorithms with fitting accuracy of 96.5%, 91.9%, and 84.9%, respectively. The SAR rules obtained from classification tree analysis with univariate splits, which was based on three atom pair descriptors only, revealed that the main factors influencing agonist activity of arylcarboxylic acid hydrazide derivatives were the presence of a methyl or trifluoromethyl group in the benzene ring attached to the furan moiety, an alkoxy group in the aromatic ring near the methylenehydrazide linker, and two or more halogen atoms (chlorine or bromine) on one side of the dumbbell-shaped hydrazide molecule opposed by an aromatic moiety on the opposite side of the molecule. Thus, these rules represent a relatively simple classification approach for de novo design of small-molecule inducers of macrophage TNF-α production.
KW - Atom pairs
KW - Macrophage
KW - Molecular descriptors
KW - Structure-activity relationship analysis
KW - Tumor necrosis factor α
U2 - 10.1016/j.bmc.2008.08.078
DO - 10.1016/j.bmc.2008.08.078
JO - Bioorganic and Medicinal Chemistry
JF - Bioorganic and Medicinal Chemistry
10.1016/j.bmc.2008.08.078