Inverse problem in QSAR/QSPR studies for the case of topological indexes characterizing molecular shape (Kier indices)

Скворцова, М. И.; Baskin, Igor I.; Slovokhotova, O. L.; Palyulin, V. A.; Зефиров, Н. С.

doi:10.1021/ci00014a017

Cited by 95 publications

(80 citation statements)

References 4 publications

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“…[33][34] From the practical point of view, it concerns generation of molecular structures possessing desired property values. Attempts to solve this problem have been reported by Gordeeva et al, [35] Skvortsova et al, [36] and Faulon et al [37] who observed some degeneracy of solutions, when several chemical structures corresponded to one set of molecular descriptor values. As pointed out in, [38] this prevents a reverse engineering of chemical structures from molecular descriptors, but, on the other hand, can be useful to safely exchange chemical information in the form of molecular descriptors.…”

Section: Representation Of Chemical Objects In Chemoinformaticsmentioning

confidence: 99%

Chemoinformatics as a Theoretical Chemistry Discipline

Varnek

Baskin

2011

Molecular Informatics

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101

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Here, chemoinformatics is considered as a theoretical chemistry discipline complementary to quantum chemistry and force-field molecular modeling. These three fields are compared with respect to molecular representation, inference mechanisms, basic concepts and application areas. A chemical space, a fundamental concept of chemoinformatics, is considered with respect to complex relations between chemical objects (graphs or descriptor vectors). Statistical Learning Theory, one of the main mathematical approaches in structure-property modeling, is briefly reviewed. Links between chemoinformatics and its "sister" fields - machine learning, chemometrics and bioinformatics are discussed.

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Section: Representation Of Chemical Objects In Chemoinformaticsmentioning

confidence: 99%

Chemoinformatics as a Theoretical Chemistry Discipline

Varnek

Baskin

2011

Molecular Informatics

Self Cite

101

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“…There are also 3D-Molecule Representation of Structures based on electron diffraction (3D-MoRSE) [11], which are based on theoretical scattering curves calculated from the same transforms as used in electron diffraction studies. In addition, there are many topological indices [12][13][14][15] which may be applicable as well.…”

Section: Defining Acceptable Moleculesmentioning

confidence: 99%

A knowledge‐based approach for screening chemical structures withinde novomolecular evolution

Chu

Alsberg

2010

Journal of Chemometrics

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Despite the advantage of employing evolutionary algorithms (EAs) for de novo creation of novel molecular structures with optimal properties, the approach is hampered by sampling chemically undesirable structures. Such structures are undesirable for different reasons, such as a critical structural pattern may be ignored or too many rotational degrees of freedom exist for conformational search. A new method is presented which creates a user-defined structure filter, here referred to as the bias filter (BF), generated from a set of molecules representing typical structure types that are allowed to evolve in the de novo process. The BF can be seen as constraining the chemical structure space according to external requirements given by the user. No explicit programming of structure rules is necessary which makes the proposed method much more intuitive and user friendly than other methods commonly used in de novo applications. We have tested the proposed method in the process of evolving a set of Factor Xa inhibitors where the aim is to create molecules with optimal logP values. The de novo GeneGear system developed in our group is responsible for the corresponding evolutionary computation and bias filtering.

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“…Inverse quantitative structure-activity relationship (QSAR) analysis aims to identify values of descriptors used to generate a QSAR model that corresponds to high activity, and build structures of active compounds from these values [1][2][3][4] . The inverse QSAR process is challenging since numerical signatures of activity, if they can be determined, must be re-translated into viable chemical structures and active compounds, a task falling into the area of de novo compound design [5][6][7] .…”

Section: Introductionmentioning

confidence: 99%

“…The inverse QSAR process is challenging since numerical signatures of activity, if they can be determined, must be re-translated into viable chemical structures and active compounds, a task falling into the area of de novo compound design [5][6][7] . A predominant approach to inverse QSAR is the use of multiple linear regression (MLR) models to construct chemical graphs that correspond to an MLR equation [1][2][3][4] . Given this equation, a desired y (activity) value constrains relationships between descriptor settings.…”

Section: Introductionmentioning

confidence: 99%

Exploring differential evolution for inverse QSAR analysis

2017

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Inverse quantitative structure-activity relationship (QSAR) modeling encompasses the generation of compound structures from values of descriptors corresponding to high activity predicted with a given QSAR model. Structure generation proceeds from descriptor coordinates optimized for activity prediction. Herein, we concentrate on the first phase of the inverse QSAR process and introduce a new methodology for coordinate optimization, termed differential evolution (DE), that originated from computer science and engineering. Using simulation and compound activity data, we demonstrate that DE in combination with support vector regression (SVR) yields effective and robust predictions of optimized coordinates satisfying model constraints and requirements. For different compound activity classes, optimized coordinates are obtained that exclusively map to regions of high activity in feature space, represent novel positions for structure generation, and are chemically meaningful.

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Inverse problem in QSAR/QSPR studies for the case of topological indexes characterizing molecular shape (Kier indices)

Cited by 95 publications

References 4 publications

Chemoinformatics as a Theoretical Chemistry Discipline

Chemoinformatics as a Theoretical Chemistry Discipline

A knowledge‐based approach for screening chemical structures withinde novomolecular evolution

Exploring differential evolution for inverse QSAR analysis

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