QSAR/QSPR

The method of building predictive models in QSPR/QSAR can also be applied to the modeling of materials without a unique, clearly defined structure. Instead of the connection table, physicochemical data as well as spectra reflecting the compound s structure can be used as molecular descriptors for model building,... 

The QSPR/QSAR methodology can also be applied to materials and mixtures where no structural information is available. Instead of descriptors derived from the compound s structure, various physicochemical properties, including spectra, can be used. In particular, spectra are valuable in this context as they reflect the structure in a sensitive way. 

Duchowicz PR, Castro EA, Toropov AA, Benfenati E (2006) Applications of Flexible Molecular Descriptors in the QSPR-QSAR Study of Heterocyclic Drugs. 3 1-38... 

Multivariate calibration has the aim to develop mathematical models (latent variables) for an optimal prediction of a property y from the variables xi,..., jcm. Most used method in chemometrics is partial least squares regression, PLS (Section 4.7). An important application is for instance the development of quantitative structure—property/activity relationships (QSPR/QSAR). 

In many cases of practical interest, no theoretically based mathematical equations exist for the relationships between x and y we sometimes know but often only assume that relationships exist. Examples are for instance modeling of the boiling point or the toxicity of chemical compounds by variables derived from the chemical structure (molecular descriptors). Investigation of quantitative structure-property or structure-activity relationships (QSPR/QSAR) by this approach requires multivariate calibration methods. For such purely empirical models—often with many variables—the... 

Among many theoretical approaches, the quantitative structure-property/ activity relationships (QSPR/QSAR) methods in conjunction with experimental data pave the way to characterization of properties of new compounds. Properly calibrated such methods provide tools for the prediction of physicochemical parameters (QSPR) and/or biological activity (QS AR) for substances, which have not been yet examined in experiments (Wiener, 1947a, b, 1948a, b Randic and Basak, 1999, 2001 Randic and Pompe, 2001a, b Basak et al., 2001). 

It is to be noted that the QSPR/QSAR analysis of nanosubstances based on elucidation of molecular structure by the molecular graph is ambiguous due to a large number of atoms involved in these molecular systems. Under such circumstances the chiral vector can be used as elucidation of structure of the carbon nanotubes (Toropov et al., 2007c). The SMILES-like representation information for nanomaterials is also able to provide reasonable good predictive models (Toropov and Leszczynski, 2006a). 

In this chapter, the genesis of SMILES-based descriptors (as well as perspectives of utilization of these characteristics for QSPR/QSAR analyses) is discussed. We concluded that in fact the SMILES-based optimal descriptors are derivatives of the graph-based optimal descriptors. In fact the SMILES-based descriptors are calculated with scheme that is similar to the well-known additive scheme (Zinkevich et al., 2004), but instead of contributions for the molecular fragments (chemical elements, different kinds of cycles, covalent bonds, etc.) contributions for the SMILES fragments (c, C, n, N, Cl, Br, =,, etc.) are using. 

Horvath, D. In QSPR/QSAR Studies by Molecular Descriptors, Diudea, M. (Ed.). Nova Science Publishers, New York,... 

Horvath D. (2001b) ComPharm—Automated comparative analysis of phar-macophoric patterns and derived QSAR approaches, novel tools in high throughput drug discovery. A proof-of-concept study applied to farnesyl protein transferase inhibitor design. In M Diudea (ed), QSPR/QSAR Studies by Molecular Descriptors, pp. 395-439, Nova Science Publishers, New York, USA. 

A.A. Toropov, E. Benfenati, SMILES in QSPR/QSAR modeling Results and perspectives. Curr. Drug Dis. Technol. 4, 77-116 (2007)... 

Optimization of predietions ean be made utilizing linear as well as nonlinear relationships by means of statistieal methods to correlate chemical and physiological descriptors to experimental datasets. These statistical methods inelude multilinear partial least square analysis, principal component analysis, and neural networking. Many of these tools are included in QSPR/QSAR packages through companies sueh as Advanced Chemistry Development, SemiChem, EduSoft, BioByte, TOPKAT, MDL, ChemSilico, Pallas, Pharma Algorithms, and others. 

Diudea MV (ed) (2001) QSPR/QSAR studies hy molecular descriptors. Nova Science, Himgtington, New York... 

Applications of Flexible Molecular Descriptors in the QSPR-QSAR Study of Heterocyclic Drugs... 

Knowledge-based Modelling QSPR/QSAR Methods and Neural Networks. 

A stepwise selection procedure is performed to search for QSPR/QSAR models after the preliminary exclusion of - constant and near-constant variables. The - pair correlation cutoff selection of variables is then performed to avoid highly correlated descriptor variables within the model. 

Bonchev, D, (2000). Overall Connectivity and Topological Complexity A New Tool for QSPR/ QSAR. In Topological Indices and Related Descriptors in QSAR and QSPR (Devillers, J. and Balaban, A.T., eds.), Gordon Breach, Amsterdam (The Netherlands), pp. 361-401. 

Diudea, M.V., Kacso, I.E. and Topan, M.I. (1996a). Molecular Topology.l8. A QSPR/QSAR Study by Using New Valence Group Carbon-Related Electronegativities. Rev.Roum.Chim., 41,141-157. 

Estrada, E. (2000). Edge-Cormectivity Indices in QSPR/QSAR Studies. 2. Accounting for Long-Range Bond Contributions. J.Chem.InfComput.ScL, 40,1042-1048. 

Lucic, B. and Trinajstic, N. (1997). New Developments in QSPR/QSAR Modeling Based on Topological Indices. SAR QSAR Environ.Res., 7,45-62. 

One solution to this quagmire has been the use of calculated properties estimated from the molecular structure of chemicals instead of their experimental data. Molecular descriptors calculated using different variations of the chemical stmcture lead to the development of quantitative structure-property/activity relationship (QSPR/QSAR) models. 

Quantitative Structure-Property and Structure-Activity Relationships (QSPR/QSAR)... 

The QSPR/QSAR methods have many direct benefits like property prediction, target molecular design, and structural refinement, and indirectly it can help to... 

The chemical space is defined as the p-dimensional space constituted by a set of p molecular descriptors selected to represent the studied compounds chemical space design is generally recognized as a crucial step for the successful application of QSPR/QSAR methods [Oprea, Zamora et al, 2002 Dutta, Guha et al, 2006 Eckert, Vogt et al, 2006 Landon and Schaus, 2006]. 

Balaban, A.T. (2001) A personal view about topological indices for QSAR/QSPR, in QSPR/ QSAR Studies by Molecular Descriptors (ed. M.V. Diudea), Nova Science, Hrmtington, NY, pp. 1-30. 

Baskin, 1.1., Halberstam, N.M., Artemenko, N.V., Palyulin, V.A. and Zefirov, N.S. (2003) NASAWIN -a universal software for QSPR/QSAR studies, in Designing Drugs and Crop Protectants Processes, Problems and Solutions (ed. M. Ford), Blackwell Publishing, Oxford, UK, pp. 260-263. 

Bonchey D. (2000) Overall connectivities/topological complexities a new powerful tool for QSPR/ QSAR./. Chem. Inf. Comput. Sci., 40, 934-941. 

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