QSPR-modelling of octanol/water partition coefficient by means of the nearest neighboring codes correlation weighing

A. A. Toropov; I. N. Nurgaliev; O. I. Balakhonenko; N. L. Voropaeva; I. N. Ruban; S. Sh Rashidova

QSPR-modelling of octanol/water partition coefficient by means of the nearest neighboring codes correlation weighing

A. A. Toropov, I. N. Nurgaliev, O. I. Balakhonenko, N. L. Voropaeva, I. N. Ruban, S. Sh Rashidova

Istituto di Ricerche Farmacologiche "Mario Negri"

Research output: Contribution to journal › Article › peer-review

Abstract

A descriptor calculated with the so-called correlation weights of local graph invariants has been suggested. As local graph invariants, Morgan vertex degrees of zero, first, and second order, and the so-called Nearest Neighboring Codes, have been used. The numerical values of the correlation weights have been calculated by the optimization procedure, which leads to an as large as possible correlation coefficient between hydrophobicity (logP, octanol/water) and the above descriptor values, on the training set. The predictive ability of such correlation has been tested with structures of the test set, the best model being obtained by means of the correlation weighing of the Nearest Neighboring Codes.

Original language	English
Pages (from-to)	37-48
Number of pages	12
Journal	Cellulose Chemistry and Technology
Volume	39
Issue number	1-2
Publication status	Published - Jan 2005

Keywords

Codes
Correlation weight
Graph invariant
Hydrophobicity
MLR
QSPRs

ASJC Scopus subject areas

Materials Science (miscellaneous)

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title = "QSPR-modelling of octanol/water partition coefficient by means of the nearest neighboring codes correlation weighing",

abstract = "A descriptor calculated with the so-called correlation weights of local graph invariants has been suggested. As local graph invariants, Morgan vertex degrees of zero, first, and second order, and the so-called Nearest Neighboring Codes, have been used. The numerical values of the correlation weights have been calculated by the optimization procedure, which leads to an as large as possible correlation coefficient between hydrophobicity (logP, octanol/water) and the above descriptor values, on the training set. The predictive ability of such correlation has been tested with structures of the test set, the best model being obtained by means of the correlation weighing of the Nearest Neighboring Codes.",

keywords = "Codes, Correlation weight, Graph invariant, Hydrophobicity, MLR, QSPRs",

author = "Toropov, {A. A.} and Nurgaliev, {I. N.} and Balakhonenko, {O. I.} and Voropaeva, {N. L.} and Ruban, {I. N.} and Rashidova, {S. Sh}",

year = "2005",

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TY - JOUR

T1 - QSPR-modelling of octanol/water partition coefficient by means of the nearest neighboring codes correlation weighing

AU - Toropov, A. A.

AU - Nurgaliev, I. N.

AU - Balakhonenko, O. I.

AU - Voropaeva, N. L.

AU - Ruban, I. N.

AU - Rashidova, S. Sh

PY - 2005/1

Y1 - 2005/1

N2 - A descriptor calculated with the so-called correlation weights of local graph invariants has been suggested. As local graph invariants, Morgan vertex degrees of zero, first, and second order, and the so-called Nearest Neighboring Codes, have been used. The numerical values of the correlation weights have been calculated by the optimization procedure, which leads to an as large as possible correlation coefficient between hydrophobicity (logP, octanol/water) and the above descriptor values, on the training set. The predictive ability of such correlation has been tested with structures of the test set, the best model being obtained by means of the correlation weighing of the Nearest Neighboring Codes.

AB - A descriptor calculated with the so-called correlation weights of local graph invariants has been suggested. As local graph invariants, Morgan vertex degrees of zero, first, and second order, and the so-called Nearest Neighboring Codes, have been used. The numerical values of the correlation weights have been calculated by the optimization procedure, which leads to an as large as possible correlation coefficient between hydrophobicity (logP, octanol/water) and the above descriptor values, on the training set. The predictive ability of such correlation has been tested with structures of the test set, the best model being obtained by means of the correlation weighing of the Nearest Neighboring Codes.

KW - Codes

KW - Correlation weight

KW - Graph invariant

KW - Hydrophobicity

KW - MLR

KW - QSPRs

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