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QSAR study

Rhyn K-B, H C Patel and A J Hopfinger 1995. A 3D-QSAR Study of Anticoccidal Triazlnes Usir Molecular Shape Analysis. Journal of Chemical Information and Computer Science 35 771-778. [Pg.741]

The field points must then be fitted to predict the activity. There are generally far more field points than known compound activities to be fitted. The least-squares algorithms used in QSAR studies do not function for such an underdetermined system. A partial least squares (PLS) algorithm is used for this type of fitting. This method starts with matrices of field data and activity data. These matrices are then used to derive two new matrices containing a description of the system and the residual noise in the data. Earlier studies used a similar technique, called principal component analysis (PCA). PLS is generally considered to be superior. [Pg.248]

Quantitative Structure—Activity Relationships (QSAR). Quantitative Stmcture—Activity Relationships (QSAR) is the name given to a broad spectmm of modeling methods which attempt to relate the biological activities of molecules to specific stmctural features, and do so in a quantitative manner (see Enzyme INHIBITORS). The method has been extensively appHed. The concepts involved in QSAR studies and a brief overview of the methodology and appHcations are given here. [Pg.168]

Quantitative Structure—Activity Relationships. Many quantitative stmcture—activity relationship (QSAR) studies of progestins have appeared in the Hterature and an extensive review of this work is available (174). QSAR studies attempt to correlate electronic, steric, and/or hydrophobic properties to progestational activity or receptor binding affinity. A review focusing on the problems associated with QSAR of steroids has been pubUshed (175). [Pg.220]

H Kubmyi. Variable selection m QSAR studies. I. An evolutionary algorithm. Quant Struct-Act Relat 13 285-294, 1994. [Pg.367]

T Kimura, K Hasegawa, K Funatsu. GA strategy for variable selection m QSAR studies GA-based region selection for CoMFA modeling. J Chem Inf Comput Sci 38 276-282, 1998. [Pg.367]

S. R Gupta, QSAR studies on drugs acting at the central nervous system. Chem. Rev. 1989, 89, 1765-1800. [Pg.108]

When applied to QSAR studies, the activity of molecule u is calculated simply as the average activity of the K nearest neighbors of molecule u. An optimal K value is selected by the optimization through the classification of a test set of samples or by the leave-one-out cross-validation. Many variations of the kNN method have been proposed in the past, and new and fast algorithms have continued to appear in recent years. The automated variable selection kNN QSAR technique optimizes the selection of descriptors to obtain the best models [20]. [Pg.315]

In 1868 two Scottish scientists, Crum Brown and Fraser [4] recognized that a relation exists between the physiological action of a substance and its chemical composition and constitution. That recognition was in effect the birth of the science that has come to be known as quantitative structure-activity relationship (QSAR) studies a QSAR is a mathematical equation that relates a biological or other property to structural and/or physicochemical properties of a series of (usually) related compounds. Shortly afterwards, Richardson [5] showed that the narcotic effect of primary aliphatic alcohols varied with their molecular weight, and in 1893 Richet [6] observed that the toxicities of a variety of simple polar chemicals such as alcohols, ethers, and ketones were inversely correlated with their aqueous solubilities. Probably the best known of the very early work in the field was that of Overton [7] and Meyer [8], who found that the narcotic effect of simple chemicals increased with their oil-water partition coefficient and postulated that this reflected the partitioning of a chemical between the aqueous exobiophase and a lipophilic receptor. This, as it turned out, was most prescient, for about 70% of published QSARs contain a term relating to partition coefficient [9]. [Pg.470]

A key requirement of QSAR is that the compounds used in the modeling and prediction processes should have the same mechanism of action, and for this reason most QSAR studies are made with congeneric series of compounds. However, if a diverse set of compounds can reasonably be assumed to have the same mechanism of action, QSAR modeling can justihably be carried out. For example, Dearden et al. [43] developed a QSAR for the ratio of brain levels of 22 very diverse drugs in the wild-type mouse and the P-glycoprotein knockout mouse (R+/ ) ... [Pg.479]

Dearden JC, AI-Noobi A, Scott AC, Thomson SA. QSAR studies on P-glycoprotein-regulated multidrug resistance and on its reversal by phenothia-zines. SAR QSAR Environ Res 2003 14 447-54. [Pg.490]

Benigni R, Giuliani A. Quantitative structure-activity relationship (QSAR) studies of mutagens and carcinogens. Med Res Rev 1996 16 267-84. [Pg.490]

A httle number of works performing Bfx and Fx quantitative structure-activity relationship (QSAR) studies have been described. On the other hand, to gain insight into the biological behavior of Bfxs and Fxs some studies... [Pg.296]

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... [Pg.310]

Gupta SP (2006) QSAR Studies on Calcium Channel Blockers. 4 249-287 Gupton JT (2006) Pyrrole Natm-al Products with Antitimior Properties. 2 53-92 Gute BD, see Basak SC (2006) 3 39-80... [Pg.310]

Ponnuswamy MN, Gromiha MM, Sony SMM, Saraboji K (2006) Conformational Aspects and Interaction Studies of Heterocyclic Drugs. 3 81-147 Prabhakar YS, Solomon VR, Gupta MK, Katti SB (2006) QSAR Studies on Thiazolidines A Biologically Privileged Scaffold. 4 161-248... [Pg.312]

Tab. 7.1 The four components of SAR and QSAR studies (modified from Ref. [4]). Tab. 7.1 The four components of SAR and QSAR studies (modified from Ref. [4]).
From a methodological viewpoint, our results suggest that range and sensitivity are useful descriptors of property spaces and can parameterize the capacity of a given molecule to span broad conformational and property spaces. In other words, range and sensitivity appear as promising descriptors of the dynamic behavior of a molecule. Their application to other dynamic QSAR studies [in particular, absorphon, distribution, metabolism and excretion (ADME) behavior] is under investigahon. [Pg.21]

Descriptors used to characterize molecules in QSAR studies should be as independent of each other (orthogonal) as possible. When using correlated parameters there is an increased danger of obtaining non-predictive, chance correlation [56]. To examine the correlation between PSA (calculated according to the fragment-based protocol [10]) and other descriptors, we studied a collection of 7010 bioactive molecules from the PubChem database [57]. In addition to PSA, the following parameters were used ... [Pg.121]

All of these parameters (with the possible exception of SAP) are frequently used in QSAR studies or as filters in virtual screening. The SAP descriptor was included to check for correlations between PSA and quantum chemically calculated charges. [Pg.122]

This chapter describes and classifies H-bond descriptors, and indicates possible areas of their application in QSAR studies and drug design. Similar analyses were presented in previous articles [3-5]. [Pg.129]

Application of H-bond Descriptors in QSAR Studies and Drug Design... [Pg.142]

H-bonding is an important, but not the sole, interatomic interaction. Thus, total energy is usually calculated as the sum of steric, electrostatic, H-bonding and other components of interatomic interactions. A similar situation holds with QSAR studies of any property (activity) where H-bond parameters are used in combination with other descriptors. For example, five molecular descriptors are applied in the solvation equation of Kamlet-Taft-Abraham excess of molecular refraction (Rj), which models dispersion force interactions arising from the polarizability of n- and n-electrons the solute polarity/polarizability (ir ) due to solute-solvent interactions between bond dipoles and induced dipoles overall or summation H-bond acidity (2a ) overall or summation H-bond basicity (2(3 ) and McGowan volume (VJ [53] ... [Pg.142]

Calculated molecular descriptors including H-bond parameters were used for QSAR studies on different types of permeabiUty. For example, the new H-bond descriptor characterizing the total H-bond ability of a compound, was successfully appUed to model Caco-2 cell permeability of 17 drugs [30]. A similar study on human jejunal in vivo permeabiUty of 22 structurally diverse compounds is described in Ref. [62]. An exceUent one-parameter correlation of human red ceU basal permeabiUty (BP) was obtained using the H-bond donor strength [63] ... [Pg.145]

QSAR studies of the pH-dependent partitioning of acidic and basic drugs into liposomes [64] yielded following equations ... [Pg.145]


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See also in sourсe #XX -- [ Pg.2 , Pg.3 , Pg.5 , Pg.279 ]

See also in sourсe #XX -- [ Pg.308 ]

See also in sourсe #XX -- [ Pg.36 ]




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3D-QSAR studies

A case study of QSARs with discrete values

Alcohols QSAR studies

Antimicrobial peptides QSAR studies

Application of H-bond Descriptors in QSAR Studies and Drug Design

Biological QSAR studies

D-QSAR Studies

Dihydrofolate reductase inhibitors QSAR studies of inhibition

Hansch approach QSAR study using

Molecular Docking and 3D-QSAR Studies

Organic QSAR study

Pharmaceutical QSAR studies

Planning a QSAR study

Properties of Metals and Metal Ions Related to QSAR Studies

Properties of Metals and Metal Ions as Tools in Quantitative Structure-Activity Relationship (QSAR) Studies

Protease inhibitors QSAR studies

Protein-ligand interactions QSAR studies

QSAR

QSAR Studies on ABC Transporter - How to Deal with Polyspecificity

QSAR studies descriptor-based pharmacophores

QSAR studies/models

Quantitative structure-activity studies (QSAR

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