QSAR


Nevertheless, this type of analysis, usually done by chromatography, is not always justified when taking into account the operator s time. Other quicker analyses are used such as FIA (Fluorescent Indicator Analysis) (see paragraph 3.3.5), which give approximate but usually acceptable proportions of saturated, olefinic, and aromatic hydrocarbons. Another way to characterize the aromatic content is to use the solvent s aniline point the lowest temperature at which equal volumes of the solvent and pure aniline are miscible.  [c.274]

Calculational methods. Associating the analysis, the knowledge of the property-structure relationships, and the calculation methods has made possible the replacement of costly and arduous test methods by quicker tests whose results are linked by calculations to the characteristic under study. Some examples are the cetane number, in some cases, the octane number, or the characteristics of LPG (refer to Chapter 3).  [c.296]

Finally, the band pass filters corresponding to the Morlet wavelet have a "quicker" decrease towards null frequencies than filters obtained with the first derivative of gaussian wavelet (fig. 9). As a result, they  [c.362]

A different design of instrument called a diode array spectrometer has become popular in recent years. In this instrument the light from the lamp passes tlnough tire sample, then into a spectrometer to be dispersed, and then is focused onto an array of solid-state detectors arranged so that each detector element measures intensity in a narrow band of wavelengdis—say one detector for each nanometre of the visible and ultraviolet regions. The output is digitized and the spectrum displayed on a screen, and it can be read out m digital fonn and processed with a computer. The complete spectrum can be recorded in a few seconds. This is not fonnally a double-beam instrument, but because a spectrum is taken so quickly and handled so easily, one can record tlie spectrum of a reference cell and the sample cell and then compare them in the computer, so it serves the same purpose. The available instruments do not give quite the resolution or versatility of the standard spectrophotometers, but they are far quicker and easier to use.  [c.1122]

This is the domain of establishing Structure-Property or Structure-Activity Relationships (SPR or SAR), or even of finding such relationships in a quantitative manner (QSPR or QSAR).  [c.3]

Scaling is quite often applied in chemometrics. However, before we start to examine it, we have to consider the following. Classical chemometrics often deals with tasks which differ from those in molecular design. An important chemome-trical task is to model experimental data, i.e., the independent variables are measured experimentally in just the same way as the responses. On the other hand, in molecular design and QSAR these independent variables are mostly calculated by some method or other. Even the physical properties of molecules, which are ap-phed as descriptors, are often evaluated theoretically. It leads to a situation where the variables have more or less similar ranges in their numerical values. Quite a different picture occurs when a model is based on experimental data. The latter can differ widely from each another, and this difference can result in misleading models, especially when PCA or PLS is used as a mapping device. Therefore, the major task of molecular design is to establish better descriptors and to select the optimal subset of them. It is noteworthy that only very few papers were published in the field of molecular design where data pre-processing techniques were used. One area where the optimal subset selection problem is crucial in chemometrics is the case where spectral data (wavelengths) are used as independent variables. Robust wavelength selection is an exciting problem.  [c.214]

The search for structural fragments (substructures) is very important in medicinal chemistry, QSAR, spectroscopy, and many other fields in the process of perception of pharmacophore, chromophore, or other -phores.  [c.291]

The search for structural fragments (substructures) is very important in medicinal chemistry, QSAR, spectroscopy and many other fields In the process of pharmacophore, chromophore or other -phore perceptions.  [c.314]

D substructure search is usually known as pharmacophore searching in QSAR. In all of the 3D search methods the conformational flexibility creates considerable difficulties.  [c.315]

All the techniques described above can be used to calculate molecular structures and energies. Which other properties are important for chemoinformatics Most applications have used semi-empirical theory to calculate properties or descriptors, but ab-initio and DFT are equally applicable. In the following, we describe some typical properties and descriptors that have been used in quantitative structure-activity (QSAR) and structure-property (QSPR) relationships.  [c.390]

The MEP at the molecular surface has been used for many QSAR and QSPR applications. Quantum mechanically calculated MEPs are more detailed and accurate at the important areas of the surface than those derived from net atomic charges and are therefore usually preferable [Ij. However, any of the techniques based on MEPs calculated from net atomic charges can be used for full quantum mechanical calculations, and vice versa. The best-known descriptors based on the statistics of the MEP at the molecular surface are those introduced by Murray and Politzer [44]. These were originally formulated for DFT calculations using an isodensity surface. They have also been used very extensively with semi-empirical MO techniques and solvent-accessible surfaces [1, 2]. The charged polar surface area (CPSA) descriptors proposed by Stanton and Jurs [45] are also based on charges derived from semi-empirical MO calculations.  [c.393]

To know what QSAR and QSPR are, and the steps in QSAR/QSPR.  [c.401]

To understand the recommendations for structure descriptors in order to be able to apply them in QSAR or drug design in conjunction with statistical methods or machine learning techniques.  [c.401]

Figure 8-1. The general QSPR/QSAR problem, Figure 8-1. The general QSPR/QSAR problem,
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,  [c.402]

Before the comparative molecular field analysis (CoMFA), BCUT descriptors, 4D-QSAR, and HYBOT descriptors arc discussed in more detail, some further descriptors are listed briefly.  [c.427]

Besides the aforementioned descriptors, grid-based methods are frequently used in the field of QSAR quantitative structure-activity relationships) [50]. A molecule is placed in a box and for an orthogonal grid of points the interaction energy values between this molecule and another small molecule, such as water, are calculated. The grid map thus obtained characterizes the molecular shape, charge distribution, and hydrophobicity.  [c.428]

Hopfinger et al. [53, 54] have constructed 3D-QSAR models with the 4D-QSAR analysis formahsm. This formalism allows both conformational flexibility and freedom of alignment by ensemble averaging, i.e., the fourth dimension is the dimension of ensemble sampling. The 4D-QSAR analysis can be seen as the evolution of Molecular Shape Analysis [55, 56].  [c.429]

In 4D-QSAR, a grid is used to determine the regions in 3D space responsible for binding. Nevertheless, neither a probe nor interaction energy is used.  [c.429]

Figure 8-15. Extension of the QSAR method by descriptors not based on structure. Figure 8-15. Extension of the QSAR method by descriptors not based on structure.
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.  [c.433]

This makes PLS an attractive method for QSAR (see Section 10.4).  [c.449]

A wide field of applications for chemical data mining is drug design. In short, drug design starts with a compound which has an interesting biological profile and optimizes the compound as well as its activity (see Section 10.4). Thus, the information about the biological activity of a compound is a crucial aspect in drug design. The relationship between a structure and its biological activity is represented by so-called quantitative structure-activity relationships (QSAR) (see Section 10.4). The field of QSAR can be approached via chemical data mining Starting from the structure input, e.g., in the form of a connection table (see Section 2.5), a 2D or 3D model of the structure is calculated. Ensuing secondary information, e.g., in the form of physicochemical properties such as charges, is generated for these structures. The enhanced structure model is then the basis for calculating a descriptor, i.e., a structure code in the form of a vector to which computational methods, for example statistical methods or neural networks, can be applied. These methods can then fulfill various data mining tasks such as classification or establishment of QSAR models which can finally be employed for the prediction of properties such as biological activities.  [c.474]

Furthermore, QSPR models for the prediction of free-energy based properties that are based on multilinear regression analysis are often referred to as LFER models, especially, in the wide field of quantitative structure-activity relationships (QSAR).  [c.489]

Figures 10.1-7 and 10.1-8 differ because the toxicity of the compounds of Figure 10.1-7 is caused solely by their tendency to move into biological membranes and is often referred to as the baseline toxicity - a property that every compound has. However, in addition to that, many compounds can interact with more specific targets. So it is necessary to have QSAR-equations with additional terms that account for electronic and steric effects. However, these are much harder to model and most models are based on the common chemical class of compounds. As a result, we face the problem today that there is a huge number of QSAR equations of a very local nature. The methods applied range from quite simple extension of Eq. (15) to CoMFA models. The descriptors also cover a wide range of types, with global whole-molecule descriptors being used the most often [34]. A major problem is the choice of the appropriate QSAR for the prediction of a new compounds, and there is a great danger of applying inappropriate QSARs. One suggestion of a way to resolve this problem is to establish models that are based on a common MOA Figures 10.1-7 and 10.1-8 differ because the toxicity of the compounds of Figure 10.1-7 is caused solely by their tendency to move into biological membranes and is often referred to as the baseline toxicity - a property that every compound has. However, in addition to that, many compounds can interact with more specific targets. So it is necessary to have QSAR-equations with additional terms that account for electronic and steric effects. However, these are much harder to model and most models are based on the common chemical class of compounds. As a result, we face the problem today that there is a huge number of QSAR equations of a very local nature. The methods applied range from quite simple extension of Eq. (15) to CoMFA models. The descriptors also cover a wide range of types, with global whole-molecule descriptors being used the most often [34]. A major problem is the choice of the appropriate QSAR for the prediction of a new compounds, and there is a great danger of applying inappropriate QSARs. One suggestion of a way to resolve this problem is to establish models that are based on a common MOA
S. P. Bradbury, SAR QSAR Environ. Res. 1994, 2. 89-104.  [c.514]

In particular, in silico methods are expected to speed up the drug discovery process, to provide a quicker and cheaper alternative to in vitro tests, and to reduce the number of compounds with unfavorable pharmacological properties at an early stage of drug development. Bad ADMET profiles are a reason for attrition of new drug candidates during the development process [9, 10]. The major reasons for attrition of new drugs are  [c.598]

The establishment of QSAR/QSPR models. This process is explained in more detail in Chapter 8. Good QSAR/QSPR models should be interpretable and guide the further development of a new drug. The computer system PASS prediction of activity spectra for substances) allows to predict simultaneously more than 500 biological activities. Among these activities are pharmacological main and side effects, mechanism of action, mutagenicity, carcinogenicity, teratogenicity, and embryotoxicity [19].  [c.605]

When an owner decides upon a more comprehensive inspection programme for the detection of fatigue cracking in the cargo holds of tankers the preferred method is likely to be magnetic particle inspection. Cracks in the vicinity of the connections between longitudinals, transverse frames and the associated lugs and brackets can run in any of several directions. It is quicker to search these areas by using yoke magnetisation with colour contrast dry powder than by any other method. Penetrant testing although portable would be of limited value due to the contamination likely to be present within any cracks and due also to the possibility of excessive cleaning at the removal stage. Electromagnetic methods such as ACFM (Alternating Current Flux Measurement) could be used but the reliability of this method is influenced by crack direction relative to scanning direction, by change in direction of section, and also by probe head design relative to weld profile. ACFM has a big advantage in circumstances where crack orientation is predictable and where only minimal cleaning is required. The welds most suitable for inspection by ACFM would include the long continuous connections of bulkheads and hopper plating to inner bottom plating or connections transverse bulkheads to lower shelf plates.  [c.1047]

Ever since it became apparent that many proteins can refold from a denatured random coil into a fully functional enzyme, implying that the amino acid sequence alone encodes the necessary infonnation, this field has been divided into two the experimental detennination and the prediction of stmcture from sequence. It may be that one day it will be much easier and quicker to compute stmctures from sequences (section C2.14.2.2), but at present the experimental detennination is more reliable. The choice is between classical methods capable of yielding three-dimensional coordinates of many or all the atoms in the molecule, which is however not under in vivo conditions and a panoply of diverse methods capable of lower resolution, or of elucidating only one particular aspect of stmcture, but under physiological conditions. Nowadays it is realized that the detennination of stmcture is a problem of inference using data from diverse sources which must be combined, and no one method is universally applicable. It is furthennore as well to remember that biological stmcture is set in a dynamical context, and that the characteristic patterns of biopolymer stmctural fluctuations are probably essential to understanding functional mechanisms Ageno [16] gives an excellent example of the consequences of rapid transitions between different confonnational states of DNA.  [c.2817]

Queisser H J 1998 Defects in semiconductors some fatal, some vital Science 281 945  [c.2897]

But why are 3D structures needed. Part of the answer to this question has already been given. As mentioned previously, a large variety of physical, chemical, and biological properties of a molecule are strongly dependent on its 3D structure. Therefore, studies which try to correlate chemical structures with a certain property under consideration - so-called QSAR/QSPl studies (Quantitative/Quahtative Structure-Activity/Property Relationship) - may gain more insight into the problem under investigation if 3D structural information is used. Modeling and prediction of biological activity, virtual screening and docking experiments (prediction of receptor/ligand interactions and complexes in biological systems), or investigations to model the chemical reactivity of a compound clearly require information on the 3D structure of the molecules imder consideration. In addition, the results of structure elucidation techniques which are based on experimental data, such as those obtained from X-ray crystallography, NMR or IR spectra, depend heavily on the quality of the initial geometries of the molecules during the structure refinement procedure. Furthermore, quantum mechanical or molecular mechanical calculations need at least a crude 3D molecular model as starting geometry.  [c.96]

D substructure search is usually known as pharmacophore searching in QSAR. Generally speaking, there are two major approaches to it topological and chemical function queries. These two techniques are based on a slighfly different philosophy and usually provide different results [31].  [c.314]

In general, a QSPR/QSAR study starts from a structure database. The molecular structitrc of each compound is entered and stored, providing information about -at least - the molecule s topology (suitable formats are discussed in Sections 2.4 and 2.9. If molecular descriptors are derived from the compound s 3D structure, both experimental and calculated geometries are used. Calculated geometries are submitted to a conformational analysis in order to restrict the study to low-cncrgy conformations. Based on the structure database, a variety of descriptors can be calculated. Optional descriptor subsets are selected. Statistical methods like multilinear regression analysis, or artificial neural networks such as backpropagation neural networks, arc applied to build models. These models relate the descriptors with the property or activity of interest. Finally, the models are validated with an external data set which has not been used for the construction of the model. The steps of a typical QSPR/QSAR study arc summarised as  [c.402]

Quantum chemical descriptors such as atomic charges, HOMO and LUMO energies, HOMO and LUMO orbital energy differences, atom-atom polarizabilities, super-delocalizabilities, molecular polarizabilities, dipole moments, and energies sucb as the beat of formation, ionization potential, electron affinity, and energy of protonation are applicable in QSAR/QSPR studies. A review is given by Karelson et al. [45].  [c.427]

The abbreviation QSAR stands for quantitative structure-activity relationships. QSPR means quantitative structure-property relationships. As the properties of an organic compound usually cannot be predicted directly from its molecular structure, an indirect approach Is used to overcome this problem. In the first step numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical methods and artificial neural network models are used to predict the property or activity of interest, based on these descriptors or a suitable subset. A typical QSAR/QSPR study comprises the following steps structure entry or start from an existing structure database), descriptor calculation, descriptor selection, model building, model validation.  [c.432]

O. A. Raevsky, V. Y. Grigor ev, E. Med-nikova, QSAR H-bonding descriptors, in Trends in Q,SAR and Molecular Modelling. C.G. Wermuth (ed.), Escom, Leiden, 1993, pp. 116-119.  [c.437]

Toxic effects are measured through a wide variety of tests. Roughly, one can distinguish two types in vivo and in vitro tests. In vivo tests are carried out with organisms such as rodents, fish, water fleas, earthworms, and algae. In-vitro tests are done mostly with single cells, organelles like mitochondria, or even just enzymes that are affected by a toxicant. The dassical" in vivo test for acute toxicity of a chemical is the LCso-value. This is the concentration at which 50% of the test spedes are lolled by the toxic effects of a compound in a given time period. Until now this has also been one of the most common values to be predicted with QSAR equations.  [c.504]

The fundamental assumption of SAR and QSAR (Structure-Activity Relationships and Quantitative Structure-Activity Relationships) is that the activity of a compound is related to its structural and/or physicochemical properties. In a classic article Corwin Hansch formulated Eq. (15) as a linear frcc-cncrgy related model for the biological activity (e.g.. toxicity) of a group of congeneric chemicals [37, in which the inverse of C, the concentration effect of the toxicant, is related to a hy-drophobidty term, FI, an electronic term, a (the Hammett substituent constant). Stcric terms can be added to this equation (typically Taft s steric parameter, E,).  [c.505]

Metabolism is still a barrier to be overcome. Some QSAR, pharmacophore, protein, and rule-based models are available to predict substrates and inhibitors of a specific cytochrome P450 isoenzyme [47-55].  [c.608]


See pages that mention the term QSAR : [c.443]    [c.429]    [c.433]    [c.435]    [c.437]    [c.491]    [c.538]    [c.594]    [c.606]    [c.617]    [c.618]   
See chapters in:

Computational chemistry  -> QSAR


Molecular modelling Principles and applications (2001) -- [ c.695 , c.696 , c.697 , c.698 , c.699 , c.700 , c.701 , c.702 , c.703 , c.704 , c.705 , c.710 , c.711 ]

Computational chemistry (2001) -- [ c.108 , c.114 , c.367 ]