Quantitative structure–activity relationship
About: Quantitative structure–activity relationship is a(n) research topic. Over the lifetime, 7560 publication(s) have been published within this topic receiving 144670 citation(s). The topic is also known as: QSAR & Quantitative Structure-Activity Relationship.
09 May 1996-Chemical Reviews
TL;DR: Applications of quantum chemical descriptors in the development of QSAR/QSPR dealing with the chemical, physical, biochemical, and pharmacological properties of compounds are reviewed.
Abstract: Quantitative structure-activity and structureproperty relationship (QSAR/QSPR) studies are unquestionably of great importance in modern chemistry and biochemistry. The concept of QSAR/QSPR is to transform searches for compounds with desired properties using chemical intuition and experience into a mathematically quantified and computerized form. Once a correlation between structure and activity/property is found, any number of compounds, including those not yet synthesized, can be readily screened on the computer in order to select structures with the properties desired. It is then possible to select the most promising compounds to synthesize and test in the laboratory. Thus, the QSAR/QSPR approach conserves resources and accelerates the process of development of new molecules for use as drugs, materials, additives, or for any other purpose. While it is not easy to find successful structureactivity/property correlations, the recent exponential growth in the number of papers dealing with QSAR/ QSPR studies clearly demonstrates the rapid progress in this area. To obtain a significant correlation, it is crucial that appropriate descriptors be employed, whether they are theoretical, empirical, or derived from readily available experimental characteristics of the structures. Many descriptors reflect simple molecular properties and thus can provide insight into the physicochemical nature of the activity/ property under consideration. Recent progress in computational hardware and the development of efficient algorithms has assisted the routine development of molecular quantummechanical calculations. New semiempirical methods supply realistic quantum-chemical molecular quantities in a relatively short computational time frame. Quantum chemical calculations are thus an attractive source of new molecular descriptors, which can, in principle, express all of the electronic and geometric properties of molecules and their interactions. Indeed, many recent QSAR/QSPR studies have employed quantum chemical descriptors alone or in combination with conventional descriptors. Quantum chemistry provides a more accurate and detailed description of electronic effects than empirical methods.1 Quantum chemical methods can be applied to quantitative structure-activity relationships by direct derivation of electronic descriptors from the molecular wave function. In many cases it has been established that errors due to the approximate nature of quantum-chemical methods and the neglect of the solvation effects are largely transferable within structurally related series; thus, relative values of calculated descriptors can be meaningful even though their absolute values are not directly applicable.2 Moreover, electronic descriptors derived from the molecular wave function can be also partitioned on the basis of atoms or groups, allowing the description of various molecular regions separately. Most work employing quantum chemical descriptors has been carried out in the field of QSAR rather than QSPR, i.e. the descriptors have been correlated with biological activities such as enzyme inhibition activity, hallucinogenic activity, etc.3-6 In part this has been because, historically, the search for quantitative relationships with chemical structure started with the development of theoretical drug design methods. Quantum-chemical descriptors have also been reported to correlate the reactivity of organic compounds, octanol/water partition coefficients, chromatographic retention indices, and various physical properties of molecules.7-11 The present article reviews applications of quantum chemical descriptors in the development of QSAR/QSPR dealing with the chemical, physical, biochemical, and pharmacological properties of compounds.
01 Jan 1999-
TL;DR: This work presents a Hierarchical Approach to the Development of QSAR Models Using Topological, Geometrical and Quantum, Chemical Parameters and Algorithms and Software for the Computation of Topological Indices and Structure-Property Models.
Abstract: 1. No-Free-Lunch Molecular Descriptor in QSAR and QSPAR 2. The Graph Description of Chemical Structures 3. Matrices and Structural Descriptors Computed from Molecular Graph Distances 4. Molecular Connectivity Chi Indices for Database Analysis and Structure-Property Modeling 5. Novel Strategies in the Search of Topological Indices 6. The Electrotopological State: Structure Modeling for QSAR and Database Analysis 7. Autocorrelation Descriptors for Modeling (Eco)Toxicological Endpoints 8. A Hierarchical Approach to the Development of QSAR Models Using Topological, Geometrical and Quantum, Chemical Parameters 9. Algorithms and Software for the Computation of Topological Indices and Structure-Property Models
01 Jan 1995-
TL;DR: Molecular concepts experimental design in synthesis-planning and structure-property correlations multivariate analysis of chemical and biological data statistical validation of QSAR results.
Abstract: Molecular concepts experimental design in synthesis-planning and structure-property correlations multivariate analysis of chemical and biological data statistical validation of QSAR results.
01 Feb 1991-Journal of Medicinal Chemistry
TL;DR: A review of the literature yielded data on over 200 aromatic and heteroaromatic nitro compounds tested for mutagenicity in the Ames test and a quantitative structure-activity relationship (QSAR) has been derived for 188 congeners, showing that chemicals possessing three or more fused rings possess much greater mutagenic potency than compounds with one or two fused rings.
Abstract: A review of the literature yielded data on over 200 aromatic and heteroaromatic nitro compounds tested for mutagenicity in the Ames test using S. typhimurium TA98. From the data, a quantitative structure-activity relationship (QSAR) has been derived for 188 congeners. The main determinants of mutagenicity are the hydrophobicity (modeled by octanol/water partition coefficients) and the energies of the lowest unoccupied molecular orbitals calculated using the AM1 method. It is also shown that chemicals possessing three or more fused rings possess much greater mutagenic potency than compounds with one or two fused rings. Since the QSAR is based on a very wide range in structural variation, aromatic rings from benzene to coronene are included as well as many different types of heterocycles, it is a significant step toward a predictive toxicology of value in the design of less mutagenic bioactive compounds.
01 Feb 2003-Journal of Computer-aided Molecular Design
TL;DR: There is additional evidence that there exists no correlation between the values of q2 for the training set and accuracy of prediction (R2) for the test set and it is argued that this observation is a general property of any QSAR model developed with LOO cross-validation.
Abstract: Quantitative Structure–Activity Relationship (QSAR) models are used increasingly to screen chemical databases and/or virtual chemical libraries for potentially bioactive molecules. These developments emphasize the importance of rigorous model validation to ensure that the models have acceptable predictive power. Using k nearest neighbors (kNN) variable selection QSAR method for the analysis of several datasets, we have demonstrated recently that the widely accepted leave-one-out (LOO) cross-validated R2 (q2) is an inadequate characteristic to assess the predictive ability of the models [Golbraikh, A., Tropsha, A. Beware of q2! J. Mol. Graphics Mod. 20, 269-276, (2002)]. Herein, we provide additional evidence that there exists no correlation between the values of q2 for the training set and accuracy of prediction (R2) for the test set and argue that this observation is a general property of any QSAR model developed with LOO cross-validation. We suggest that external validation using rationally selected training and test sets provides a means to establish a reliable QSAR model. We propose several approaches to the division of experimental datasets into training and test sets and apply them in QSAR studies of 48 functionalized amino acid anticonvulsants and a series of 157 epipodophyllotoxin derivatives with antitumor activity. We formulate a set of general criteria for the evaluation of predictive power of QSAR models.