Structure-Activity Relationships on the Molecular Descriptors Family Project at the End (original) (raw)
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Quantitative Structure-Activity Relationships: A Novel Approach of Drug Design and Discovery
Quantitative structure-activity relationships (QSAR) play a central role in computational molecular modeling methodolo-gies, since last two decade. QSARs are cheaper and rapid alternative to the medium throughput in vitro and low through-put in vivo assays which are generally restricted to later in the discovery cascade. QSAR modeling can be characterized by a collection of well defined protocols and procedures that facilitate the skilled appliance of the method for exploring and exploiting ever growing collections of biologically active chemical compounds. In the present paper the objective of QSAR in combination with their advantages will be reviewed. Authors also report various descriptors used and their selection criterion. Regression methods are crucial practices in the development of QSAR models and need to be optimized to obtain the best performance for a particular problem. Various training and test data set selection techniques are also review and summarized. Several evaluation parameters of QSAR models are summarized to assist their reliability and significance. Finally, authors also discuss assortment types of QSARs.
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Current Topics in Medicinal Chemistry, 2008
In order to minimize expensive drug failures, is essential to determine potential activity, toxicity and ADME problems as early as possible. In view of the large libraries of compounds now being handled by combinatorial chemistry and high-throughput screening, identification of potential drug is advisable even before synthesis using computational techniques such as QSAR modeling. A great number of in silico approaches to activity/toxicity prediction have been described in the literature, using molecular 0D, 1D, 2D and 3D descriptors. Also these descriptors have been implemented in available computational tools such as DRAGON, SYBYL and CODESSA for it easy use. However, many of them only have been used to explain a few prediction problems. This review attempts to summarize present knowledge related to the computational biological activity prediction based in 2D molecular descriptors implemented in the DRAGON software. These applications rely on new computational techniques such as virtual combinatorial synthesis, virtual computational screening or inverse. Several topological molecular descriptors applications are described, ranging from simple topological indices to topological indices derived from matrices weighted with atomic and bond properties. Their advantages, limitations and its possibilities in drug design are also discussed.
Variable selection and model validation of 2D and 3D molecular descriptors
Journal of Computer-aided Molecular Design, 2004
We have found that molecular shape and electrostatics, in conjunction with 2D structural fingerprints, are important variables in discriminating classes of active and inactive compounds. The subject of this paper is how to explore the selection of these variables and identify their relative importance in quantitative structure–activity relationships (QSAR) analysis. We show the use of these variables in a form of similarity searching with respect to a crystal structure of a known bound ligand. This analysis is then validated through k-fold cross-validation of enrichments via several common classifiers. Additionally, we show an effective methodology using the variables in hypothesis generation; namely, when the crystal structure of a bound ligand is not known.
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Despite the number of available chemicals growing exponentially, testing of their toxicological and environmental behavior is often a critical issue and alternative strategies are required. Additionally, there is the need to predict properties of not yet synthesized compounds to reduce the costs of synthesis, selecting only those that have the maximal potential to be active and nontoxic compounds. In order to evaluate chemical properties avoiding chemical synthesis and reducing expensive and time-demanding laboratory testing, it is necessary to build in silico models establishing a mathematical relationship between the structures of molecules and the considered properties (quantitative structure-activity relationships, QSARs). Molecular descriptors play a fundamental role in QSAR and other in silico models since they formally are the numerical representation of a molecular structure. Molecular descriptors can be classified using different criteria. Among them, there are two main categories, experimental and theoretical descriptors. The basis to understand and perform molecular descriptor calculation, the different theoretical descriptor categories together with their perspectives are described in this chapter.
Atom pairs as molecular features in structure-activity studies: definition and applications
Journal of Chemical Information and Computer Sciences, 1985
A simple type. of substructure called an atom pair is defined in terms of the atomic environments of, and shortest path separations between, all pairs of atoms in the topological representation of a chemical structure. An algorithm is presented for computing atom pairs from such a representation. Two applications of atom pairs to structure-activity problems are described. In the first, a measure of similarity between compounds is defined, and the use of this measure in probing large databases of structures is discussed. In the second, a heuristic technique called trend vector analysis is described. The trend vector summarizes the correlation, within a set of structures, of the Occurrence of atom pairs of different types with measured biological activity. These correlations can be used to estimate the biological activity of new compounds. A comparison of trend vector analysis with discriminant plane analysis is presented for one series of compounds.
International Journal of Innovative Computing, 2018
The urge of producing new chemical compounds under eco-friendly production restriction and with minimum side effects is significantly rising, considering the difficulties that conventional methods are dealing with, from financial investments and being time consuming to multi resistance microorganisms and untreatable diseases. Rational molecular design methods like Computer Aided Molecular Design (CAMD) and Quantitative Structure Activity Relationship (QSAR) allow the production of new substances with pre-decided properties and correlate the structure to biological activity, which influences the drug development process and minimizes the financial investment involved in the process. QSAR employs several descriptors to decode the molecular configuration of a compound, which facilitate the understanding of its physical and biological properties. There are various conditions such as the selection of compounds and descriptors that must be accomplished for developing an applicable model o...
Computers in Biology and Medicine, 2012
The sensitivity towards branching, discriminating power, and degeneracy of the proposed novel descriptors were investigated. Utility of these indices was investigated for development of models through decision tree and moving average analysis for the prediction of human corticotropin releasing factor-1 receptor binding affinity of substituted pyrazines. A wide variety of 46 2D and 3D molecular descriptors including proposed indices was employed for development of models through decision tree and moving average analysis. The calculation of most of these descriptors for each compound of the dataset was performed using online E-Dragon software (version 1.0). An in-house computer programme was also employed to calculate additional topological descriptors which did not figure in E-Dragon software. The decision tree classified and correctly predicted the input data with an impressive accuracy of 92% in the training set and 71% during crossvalidation. A total of three descriptors, identified by decision tree, were subsequently utilized for development of suitable models using moving average analysis. These models predicted human corticotropin releasing factor-1 receptor binding affinity with an accuracy of Z85%. The statistical significance of models was assessed through sensitivity, specificity and Matthew's correlation coefficient. High discriminating power, high sensitivity towards branching amalgamated with negligible degeneracy offer proposed descriptors a vast potential for use in the quantitative structure-activity/property/toxicity relationships so as to facilitate drug design.
Computational Methods in Developing Quantitative StructureActivity Relationships (QSAR): A Review
Combinatorial Chemistry & High Throughput Screening, 2006
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.
Chemical Structure-Biological Activity Models for Pharmacophores’ 3D-Interactions
International Journal of Molecular Sciences, 2016
Within medicinal chemistry nowadays, the so-called pharmaco-dynamics seeks for qualitative (for understanding) and quantitative (for predicting) mechanisms/models by which given chemical structure or series of congeners actively act on biological sites either by focused interaction/therapy or by diffuse/hazardous influence. To this aim, the present review exposes three of the fertile directions in approaching the biological activity by chemical structural causes: the special computing trace of the algebraic structure-activity relationship (SPECTRAL-SAR) offering the full analytical counterpart for multi-variate computational regression, the minimal topological difference (MTD) as the revived precursor for comparative molecular field analyses (CoMFA) and comparative molecular similarity indices analysis (CoMSIA); all of these methods and algorithms were presented, discussed and exemplified on relevant chemical medicinal systems as proton pump inhibitors belonging to the 4-indolyl,2-guanidinothiazole class of derivatives blocking the acid secretion from parietal cells in the stomach, the 1-[(2-hydroxyethoxy)-methyl]-6-(phenylthio)thymine congeners' (HEPT ligands) antiviral activity against Human Immunodeficiency Virus of first type (HIV-1) and new pharmacophores in treating severe genetic disorders (like depression and psychosis), respectively, all involving 3D pharmacophore interactions.