Can 3D Structural Parameters Be Predicted from 2D (Topological) Molecular Descriptors?

Estrada, Ernesto; Molina, Enrique; Perdomo-López, Iliana

doi:10.1021/ci000170v

Cited by 49 publications

(31 citation statements)

References 28 publications

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“…It stems from the fact that the geometry of a compound in many cases depends on its topology. An elegant example was provided by Estrada et al, who demonstrated that the dihedral angles of biphenyl as a function of the substituents attached to it can be predicted by topological indices [62]. Along the same line, a supposedly typically 3D property, chirality, has been predicted using chiral topological indices [63], constructed by introducing an adequate weight into the topological matrix for the chiral carbons.…”

Section: The 2d-versus 3d-qsar Approachmentioning

confidence: 99%

Computational Methods in Developing Quantitative Structure-Activity Relationships (QSAR): A Review

Dudek¹,

Arodź²,

Gálvez³

2006

CCHTS

351

238

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Virtual filtering and screening of combinatorial libraries have recently gained attention as methods complementing the high-throughput screening and combinatorial chemistry. These chemoinformatic techniques rely heavily on quantitative structure-activity relationship (QSAR) analysis, a field with established methodology and successful history. In this review, we discuss the computational methods for building QSAR models. We start with outlining their usefulness in high-throughput screening and identifying the general scheme of a QSAR model. Following, we focus on the methodologies in constructing three main components of QSAR model, namely the methods for describing the molecular structure of compounds, for selection of informative descriptors and for activity prediction. We present both the well-established methods as well as techniques recently introduced into the QSAR domain.

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Section: The 2d-versus 3d-qsar Approachmentioning

confidence: 99%

Computational Methods in Developing Quantitative Structure-Activity Relationships (QSAR): A Review

Dudek¹,

Arodź²,

Gálvez³

2006

CCHTS

351

238

View full text Add to dashboard Cite

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“…In addition, there is not any evidence that a given conformation instead of another can affect the quality of QSAR models, despite the profound effects of conformation on the biological activity. Thus, simpler methods, like those based on connectivity indices and bidimensional shape, are supposed to perform appropriately, at least in many practical cases (BROWN; MARTIN, 1997;ESTRADA et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

Employing conformational analysis in the molecular modeling of agrochemicals: insights on QSAR parameters of 2,4-D

Freitas

Ramalho

2013

Ciênc. agrotec.

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A common practice to compute ligand conformations of compounds with various degrees of freedom to be used in molecular modeling (QSAR and docking studies) is to perform a conformational distribution based on repeated random sampling, such as Monte-Carlo methods. Further calculations are often required. This short review describes some methods used for conformational analysis and the implications of using selected conformations in QSAR. A case study is developed for 2,4-dichlorophenoxyacetic acid (2,4-D), a widely used herbicide which binds to TIR1 ubiquitin ligase enzyme. The use of such an approach and semi-empirical calculations did not achieve all possible minima for 2,4-D. In addition, the conformations and respective energies obtained by the semi-empirical AM1 method do not match the calculated trends obtained by a high level DFT method. Similar findings were obtained for the carboxylate anion, which is the bioactive form. Finally, the crystal bioactive structure of 2,4-D was not found as a minimum when using Monte-Carlo/AM1 and is similarly populated with another conformer in implicit water solution according to optimization at the B3LYP/aug-cc-pVDZ level. Therefore, quantitative structure-activity relationship (QSAR) methods based on three dimensional chemical structures are not fundamental to provide predictive models for 2,4-D congeners as TIR1 ubiquitin ligase ligands, since they do not necessarily reflect the bioactive conformation of this molecule. This probably extends to other systems.Index terms: Structural analysis, 2,4-dichlorophenoxyacetic acid, theoretical calculations. RESUMOUma prática comum para determinar a conformação de ligantes em compostos com vários graus de liberdade a serem utilizados em modelagem molecular (QSAR e estudos de ancoramento molecular) é realizar uma distribuição conformacional baseada em repetidas amostragens aleatórias, tais como o método Monte Carlo. Cálculos adicionais são frequentemente necessários. Esta rápida revisão descreve alguns métodos usados para análise conformacional e as implicações ao usarem algumas conformações selecionadas em QSAR. Um estudo de caso foi desenvolvido para o ácido 2,4-diclorofenóxi acético (2,4-D), um herbicida amplamente usado que se liga à enzima TIR1 ubiquitina ligase. O uso de tal aproximação e cálculos semi-empíricos não alcançou todos os mínimos possíveis para o 2,4-D. Além disso, as conformações e respectivas energias obtidas pelo método semi-empírico AM1 não concordam com as tendências calculadas em nível DFT. Resultados similares foram obtidos para o ânion carboxilato, que é a forma bioativa do 2,4-D. Finalmente, a estrutura cristalina e bioativa do 2,4-D não foi encontrada como um mínimo de energia usando MonteCarlo/AM1 e, de acordo com otimização em nível B3LYP/aug-cc-pVDZ, tem população similar ao outro confôrmero em solução aquosa implícita. Portanto, métodos para relação quantitativa entre estrutura e atividade (QSAR) baseados na estrutura química tridimensional não são fundamentais para fornecer modelos preditivos...

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“…Moreover, one can detect the fragments on a given molecule that contribute positively or negatively (by summing up bonds contributions) to the underlying activity and forward an interpretation of their effects in terms of physicochemical properties. Another advantage of the spectral moment descriptors is their ability to explain in a reasonable way a considerable part of spatial phenomena [113] − a particular characteristic of topographical descriptors. Calculation of these spectral moment descriptors has been implemented in the versatile software MODESLAB v1.5 [114].…”

Section: Spectral Momentsmentioning

confidence: 99%

Role of Ligand-Based Drug Design Methodologies toward the Discovery of New Anti- Alzheimer Agents: Futures Perspectives in Fragment-Based Ligand Design

Speck‐Planche

Luan²,

Cordeiro³

2012

CMC

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Alzheimer's disease (AD), a degenerative disease affecting the brain, is the single most common source of dementia in adults. The cause and the progression of AD still remains a mystery among medical experts. As a result, a cure has not yet been discovered, even after decade's worth of research that started since 1906, when the disease was first identified. Despite the efforts of the scientific community, several of the biological receptors associated with AD have not been sufficiently studied to date, limiting in turn the design of new and more potent anti-AD agents. Thus, the search for new drug candidates as inhibitors of different targets associated with AD constitutes an essential part towards the discovery of new and more efficient anti-AD therapies. The present work is focused on the role of the Ligand-Based Drug Design (LBDD) methodologies which have been applied for the elucidation of new molecular entities with high inhibitory activity against targets related with AD. Particular emphasis is given also to the current state of fragment-based ligand approaches as alternatives of the Fragment-Based Drug Discovery (FBDD) methodologies. Finally, several guidelines are offered to show how the use of fragment-based descriptors can be determinant for the design of multi-target inhibitors of proteins associated with AD.

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Can 3D Structural Parameters Be Predicted from 2D (Topological) Molecular Descriptors?

Cited by 49 publications

References 28 publications

Computational Methods in Developing Quantitative Structure-Activity Relationships (QSAR): A Review

Computational Methods in Developing Quantitative Structure-Activity Relationships (QSAR): A Review

Employing conformational analysis in the molecular modeling of agrochemicals: insights on QSAR parameters of 2,4-D

Role of Ligand-Based Drug Design Methodologies toward the Discovery of New Anti- Alzheimer Agents: Futures Perspectives in Fragment-Based Ligand Design

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