The Characterization of Chemical Structures Using Molecular Properties. A Survey

Livingstone, David J.

doi:10.1021/ci990162i

Cited by 243 publications

(161 citation statements)

References 219 publications

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“…The need for interpretability depends on the application, as a validated mathematical model relating a target property to chemical features may, in some cases, be all that is necessary, though it is obviously desirable to attempt some explanation of the 'mechanism' in chemical terms, but it is often not necessary, per se (Livingstone 2000). This type of QSAR model follows a path that starts with a statistical validation and further interpretation for their biological and mechanistic meaning (Tropsha et al 2003).…”

Section: Variable Selection and Reductionmentioning

confidence: 99%

Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.

Eriksson

Jaworska

Worth

et al. 2003

Environ Health Perspect

1,184

847

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This article provides an overview of methods for reliability assessment of quantitative structure-activity relationship (QSAR) models in the context of regulatory acceptance of human health and environmental QSARs. Useful diagnostic tools and data analytical approaches are highlighted and exemplified. Particular emphasis is given to the question of how to define the applicability borders of a QSAR and how to estimate parameter and prediction uncertainty. The article ends with a discussion regarding QSAR acceptability criteria. This discussion contains a list of recommended acceptability criteria, and we give reference values for important QSAR performance statistics. Finally, we emphasize that rigorous and independent validation of QSARs is an essential step toward their regulatory acceptance and implementation.

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Section: Variable Selection and Reductionmentioning

confidence: 99%

Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.

Eriksson

Jaworska

Worth

et al. 2003

Environ Health Perspect

1,184

847

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“…2). In this context, 3D QSAR models are generated to explain the relationships between the intermolecular interactions related to the 3D conformations of a set of structurally related molecules and their experimental activity (e.g., IC 50 , K i ), therefore, providing a rational basis for the development of new promising compounds [24,28,29].…”

Section: Structure-based Drug Designmentioning

confidence: 99%

Structure-Based Drug Design Strategies in Medicinal Chemistry

Andricopulo¹,

Salum²,

Abraham

2009

CTMC

138

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A broad variety of medicinal chemistry approaches can be used for the identification of hits, generation of leads, as well as to accelerate the development of high quality drug candidates. Structure-based drug design (SBDD) methods are becoming increasingly powerful, versatile and more widely used. This review summarizes current developments in structure-based virtual screening and receptor-based pharmacophores, highlighting achievements as well as challenges, along with the value of structure-based lead optimization, with emphasis on recent examples of successful applications for the identification of novel active compounds.Keywords: Structure-based drug design, medicinal chemistry, virtual screening, QSAR, pharmacophores. STRUCTURE-BASED DRUG DESIGNThe performance of biochemical processes and cell mechanisms are dependent upon complex and multiple noncovalent intermolecular interactions between proteins and small-molecule modulators. The understanding of the structural and chemical binding properties of important drug targets in biologically relevant pathways allows the design of small molecules capable of regulating or modulating specific target functions in the body that are closely linked to human diseases and disorders, through multiple intermolecular interactions within a well-defined binding pocket [1][2][3][4]. In general, the identification of promising hits for further optimization is a major challenge faced by the both pharmaceutical and academic laboratories. Although the trial-anderror nature is inherent in drug research, rational concepts and modern computational methods have become widely employed for lead selection and optimization.The use of three-dimensional (3D) protein structure information in the development of new biologically active molecules, which is termed Structure-Based Drug Design (SBDD), is a well-established, successful and highly attarctive strategy used by academic and pharmaceutical research laboratories worldwide [3][4][5][6][7][8]. As a creative and knowledgedriven approach, an essential requirement for structure-based studies is a substantial understanding of the spatial and energetic aspects that affect the binding affinities of proteinligand complexes. Considering that the shape and chemical nature of the binding site of a specific target protein are known, and the possible intermolecular interactions between ligands and the protein within its active site have been identified, this qualified information can be directly employed for the identification of new ligands and the optimization of lead compounds. This opens new possibilities to boost the search for lead molecules and to limit the number of compounds that need to be evaluated experimentally.Hits can be identified through the docking of smallmolecule ligands (selected from databases of chemical structures) into protein active sites or by using receptorbased pharmacophore models. Furthermore, drug candidates can be designed de novo by improving the complementary binding properties of lead compounds and the r...

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“…These descriptors are atomic descriptors and are derived from quantum chemical calculations and spectroscopy. [6]. High-throughput screening method allows fast screening of large number of dataset.…”

Section: Introductionmentioning

confidence: 99%

QSAR and its Role in Target-Ligand Interaction

Singh¹,

Singh²

2013

TOBIOIJ

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Each molecule has its own specialty, structure and function and when these molecules are combined together they form a compound. Structure and function of a molecule are related to each other and QSARs (Quantitative StructureActivity relationships) are based on the criteria that the structure of a molecule must contain the features responsible for its physical, chemical, and biological properties, and on the ability to represent the chemical by one, or more, numerical descriptor(s). By QSAR models, the biological activity of a new or untested chemical can be inferred from the molecular structure of similar compounds whose activities have already been assessed. QSARs attempt to relate physical and chemical properties of molecules to their biological activities. For this there are so many descriptors (for example, molecular weight, number of rotatable bonds, Log P) and simple statistical methods such as Multiple Linear Regression (MLR) are used to predict a model. These models describe the activity of the data set and can predict activities for further sets of (untested) compounds. These types of descriptors are simple to calculate and allow for a relatively fast analysis. 3D-QSAR uses probe-based sampling within a molecular lattice to determine three-dimensional properties of molecules (particularly steric and electrostatic values) and can then correlate these 3D descriptors with biological activity. Physicochemical descriptors, include hydrophobicity, topology, electronic properties, and steric effects etc. These descriptors can be calculated empirically, statistically or through more recent computational methods. QSARs are currently being applied in many disciplines, with many pertaining to drug design and environmental risk assessment.

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The Characterization of Chemical Structures Using Molecular Properties. A Survey

Cited by 243 publications

References 219 publications

Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.

Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.

Structure-Based Drug Design Strategies in Medicinal Chemistry

QSAR and its Role in Target-Ligand Interaction

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