Pharmacophores from Binding Data

Doweyko, Arthur M.

doi:10.1021/jm00038a006

Cited by 38 publications

(18 citation statements)

References 11 publications

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“…The bioactivity of a specific compound is forecasted by summing all the partial activity values at points in common with the composite reference lattice. Some of the successful applications of HASL approach include the analysis of the in vitro antimalarial activity of artemisinin analogs [58], in vitro biochemical and in vivo gastric antisecretory activity of substituted imidazo[1,2-a]pyridines [59], sequence specificity of DNA alkylation by uracil mustard [60], and the generation of putative pharmacophoric models of the HIV-1 protease inhibitors [61]. HASL is a copyrighted program of Hypothesis Software and eduSoft LC [62], and also comes as one of the modules in Sybyl Software [33].…”

Section: Haslmentioning

confidence: 99%

3D-QSAR in Drug Design - A Review

Verma¹,

Khedkar²,

Coutinho

2010

CTMC

702

393

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Quantitative structure-activity relationships (QSAR) have been applied for decades in the development of relationships between physicochemical properties of chemical substances and their biological activities to obtain a reliable statistical model for prediction of the activities of new chemical entities. The fundamental principle underlying the formalism is that the difference in structural properties is responsible for the variations in biological activities of the compounds. In the classical QSAR studies, affinities of ligands to their binding sites, inhibition constants, rate constants, and other biological end points, with atomic, group or molecular properties such as lipophilicity, polarizability, electronic and steric properties (Hansch analysis) or with certain structural features (Free-Wilson analysis) have been correlated. However such an approach has only a limited utility for designing a new molecule due to the lack of consideration of the 3D structure of the molecules. 3D-QSAR has emerged as a natural extension to the classical Hansch and Free-Wilson approaches, which exploits the three-dimensional properties of the ligands to predict their biological activities using robust chemometric techniques such as PLS, G/PLS, ANN etc. It has served as a valuable predictive tool in the design of pharmaceuticals and agrochemicals. Although the trial and error factor involved in the development of a new drug cannot be ignored completely, QSAR certainly decreases the number of compounds to be synthesized by facilitating the selection of the most promising candidates. Several success stories of QSAR have attracted the medicinal chemists to investigate the relationships of structural properties with biological activity. This review seeks to provide a bird's eye view of the different 3D-QSAR approaches employed within the current drug discovery community to construct predictive structure-activity relationships and also discusses the limitations that are fundamental to these approaches, as well as those that might be overcome with the improved strategies. The components involved in building a useful 3D-QSAR model are discussed, including the validation techniques available for this purpose.

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Section: Haslmentioning

confidence: 99%

3D-QSAR in Drug Design - A Review

Verma¹,

Khedkar²,

Coutinho

2010

CTMC

702

393

View full text Add to dashboard Cite

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“…The consideration of the interior of a molecule according to the characteristics calculated at the lattice point is used in many well-known methods, e.g., in hypotetical active site lattice (HASL) and comparative molecular field analysis (CoMFA) [32][33][34][35]. However, in the ConGO algorithm the structures are considered on the basis of the calculated electron density agreeing closely with the results of highly accurate X-ray analysis, as shown in the next section.…”

Section: Congo Algorithmmentioning

confidence: 99%

A new paradigm for pattern recognition of drugs

Потемкин

Grishina

2008

J Comput Aided Mol Des

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A new paradigm is suggested for pattern recognition of drugs. The approach is based on the combined application of the 4D/3D quantitative structure-activity relationship (QSAR) algorithms BiS and ConGO. The first algorithm, BiS/MC (multiconformational), is used for the search for the conformers interacting with a receptor. The second algorithm, ConGO, has been suggested for the detailed study of the selected conformers' electron density and for the search for the electron structure fragments that determine the pharmacophore and antipharmacophore parts of the compounds. In this work we suggest using a new AlteQ method for the evaluation of the molecular electron density. AlteQ describes the experimental electron density (determined by low-temperature highly accurate X-ray analysis) much better than a number of quantum approaches. Herein this is shown using a comparison of the computed electron density with the results of highly accurate X-ray analysis. In the present study the desirability function is used for the first time for the analysis of the effects of the electron structure in the process of pattern recognition of active and inactive compounds. The suggested method for pattern recognition has been used for the investigation of various sets of compounds such as DNA-antimetabolites, fXa inhibitors, 5-HT(1A), and alpha(1)-AR receptors inhibitors. The pharmacophore and antipharmacophore fragments have been found in the electron structures of the compounds. It has been shown that the pattern recognition cross-validation quality for the datasets is unity.

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“…A similar pattern has been obtained from QSAR type studies using the hypothetical active-site lattice methodology (HASL). This method generated apharmacophore for the HIV-I PR inhibitors, containing 11 distinctive points or areas (Doweyko, 1994). The segment that contributed the most to the free energy of binding was found to be the part of the peptidic Table I .…”

Section: Analysis Of Inhibitor Bindingmentioning

confidence: 99%

Solvent accessibility as a predictive tool for the free energy of inhibitor binding to the HIV‐1 protease

1995

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We have developed a simple approach for the evaluation of the free energies of inhibitor binding to the protease of the human immunodeficiency virus (HIV-1 PR). Our algorithm is based on the observation that most groups that line the binding pockets of this enzyme are hydrophobic in nature. Based on this fact, we have likened the binding of an inhibitor to this enzyme to its transfer from water to a medium of lower polarity. The resulting expression produced values for the free energy of binding of inhibitors to the HIV-1 PR that are in good agreement with experimental values. The additive nature of this approach has enabled us to partition the free energy of binding into the contributions of single fragments. The resulting analysis clearly indicates the existence of a ranking in the participation of the enzyme's subsites in binding. Although all the enzyme's pockets contribute to binding, the ones that bind the P2-P; span of the inhibitor are in general the most critical for high inhibitor potency. Moreover, our method has allowed us to determine the nature of the functional groups that fit into given enzyme binding pockets. Perusal of the energy contributions of single side chains has shown that a large number of hydrophobic and aromatic groups located in the central portion of the HIV-1 PR inhibitors present optimal binding. All of these observations are in agreement with experimental evidence, providing a validation for the physical relevancy of our model.

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Pharmacophores from Binding Data

Cited by 38 publications

References 11 publications

3D-QSAR in Drug Design - A Review

3D-QSAR in Drug Design - A Review

A new paradigm for pattern recognition of drugs

Solvent accessibility as a predictive tool for the free energy of inhibitor binding to the HIV‐1 protease

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