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Pharmacophore discovery

Van Drie, John H. Pharmacophore discovery A critical review, Comput. Med. Chem. Drug Discovery 2004, 437-460. CODEN 69EIPX CAN 141 306806 AN 2004 371615 CAPLUS. [Pg.102]

Drie, J.H. (2004) Pharmacophore discovery a critical review, in Computational Medicinal Chemistry for Drug Discovery (ed. P. Bultinck), Marcel Dekker, USA, pp. 437-460. [Pg.404]

Nicklaus, M.C., Neamati, N., Hong, H., Mazumder, A., Sunder, S., Chen, J. et al. (1997) HlV-1 integrase pharmacophore discovery of inhibitors through three-dimensional database searching. [Pg.222]

Since molecules are flexible and not static, a conformational analysis has to be carried out first to generate an ensemble of low-energy conformations. This is probably one of the most critical steps in the pharmacophore discovery process, since the goal is not only to consider the global miifima of a molecule, but also to include the bioactive conformation as part of an ensemble of low-energy conformations. [Pg.575]

The Hansch method, known as quantitative structure-activity relationships (QSAR), has evolved to embrace a variety of techniques. A glance at the recently published proceedings of the European QSAR Conference [1] shows how much of an impact the methods of pharmacophore discovery have on the computational aspects of medicinal chemistry. Indeed, looking up publications that cite various pharmacophore discovery methods papers, it is surprising to see that the total has rapidly accelerated in the past few years, demanding that a review such as this sort through hundreds of papers. [Pg.438]

The final general aspect of pharmacophore discovery that must be stressed is that, fundamentally, this is a method of inference. By contrast, quantum mechanics is deductive, in that one begins with the laws of quantum mechanics to deduce consequences, the correctness of which is guaranteed by those laws. The results of inference cannot be guaranteed to be correct. Their validity can only be determined by their successful prospective application, prospective emphasizing that these models must be applied to molecules never before seen by the computational scientist. [Pg.439]

Fundamentally, pharmacophore discovery consists of looking for patterns in data. Most of these patterns will be physically irrelevant only the occasional pattern will be physically meaningful, and will be useful in guiding the medicinal chemist in deciding on which molecule to make next. [Pg.439]

This review will first briefly describe the key pharmacophore discovery methods in a linear, temporal fashion to emphasize the evolution of the concepts. Following that, selected methods will be compared and contrasted in detail, and, finally, the lessons learned over the past decade will be summarized. [Pg.439]

After ALADDIN, this author and Martin parted ways, but each played a role in the rise of two distinct pharmacophore discovery methods. In 1990, this author joined, as a founding member and first scientist, a new company in Silicon Valley, BioCAD, dedicated to the development of software to assist drug discovery. Our software, Catalyst, contained three components conformational analysis, 3D database searching, and Hypothesis Generation —a novel approach to pharmacophore discovery. Catalyst represented the first fully automated method for pharmacophore discovery. [Pg.441]

Although Catalyst was not a commercial success (the company BioCAD folded in 1994, and the software was taken over by MSI), it stimulated much interest in fully automated pharmacophore discovery. Martin et al. [16] developed DISCO, borrowing code from ALADDIN to detect features and employing a clique detection algorithm mathematically similar to the MNMM method. DISCO also relies on separate, exhaustive conformational analysis, and, in general, produces many pharmacophores consistent with the SAR. [Pg.441]

The original work of Marshall et al. has been carried on, primarily by Beusen and Shands [17a] and Dammkoehler et al. [17b]. Their work has been innovative, primarily in that the pharmacophore discovery algorithm is now integrated with the conformational analysis, a step that should, in principle, significantly improve the quality of the results. The work of SCAMPI [18] is conceptually similar to the work of Beusen and Shands. [Pg.441]

Walters and Hinds [19] at the Chicago Medical School devised the first use of a genetic algorithm applied to pharmacophore discovery, with their GERM software. The later work of Pei et al. [20], PARM, was conceptually similar to the Walters and Hinds method. These approaches allow facile integration of both feature detection and 3D analyses. [Pg.441]

After developing novel approaches to exhaustive conformational analysis, Crippen [21] at the University of Michigan took a novel approach to pharmacophore discovery, based on Voronoi polyhedra (using hyperplanes to partition space into regions encompassing active molecules). This line of investigation was ultimately abandoned, as Crippen was unable to find a satisfactory resolution to the problem of multiple solutions consistent with the SAR. [Pg.441]

The demise of BioCAD spawned two new approaches to pharmacophore discovery My former colleagues at BioCAD, joined by Barnum, introduced Hiphop [22], a variant of the MNMM approach with a statistical metric added, to alleviate some of the known problems with HypoGen. This author joined Upjohn (now Pharmacia, Kalamazoo, MI), which provided him the opportunity to develop and publish his novel pharmacophore discovery method, DANTE. The two key innovations in DANTE were its use of the principle of selectivity [23,24] to rank possible pharmacophore solutions arising from the MNMM method, and the automatic inference of sterically forbidden regions (another concept that originates from... [Pg.441]

Conformational analysis. Pharmacophore discovery inherently involves the 3D structure of molecules and, by necessity, goes beyond merely the low-energy conformation. [Pg.443]

User intervention required. Most pharmacophore discovery methods are not fully automated, but require some aspects of the solution to be provided by the user. [Pg.443]

Not Quite Pharmacophore Discovery APOLLO/Yak/Prgen, COMPASS... [Pg.443]

Semiautomated Pharmacophore Discovery Methods Developed by Marshall and Others at Washington University at St. Louis... [Pg.444]

Fully Automated Pharmacophore Discovery Catalyst s HypoGen, DISCO, Catalyst s Hiphop, DANTE... [Pg.445]

Befitting the notion that these are fully automated pharmacophore discovery methods, this is minimal, although DISCO, Hiphop, and Catalyst s HypoGen require considerable user intervention to sift through the multiple pharmacophores that emerge. Furthermore, considerable effort is advocated in selecting a representative dataset for HypoGen to avoid nonsensical results. [Pg.448]

Despite the immaturity of these methodologies of pharmacophore discovery, a tremendous variety of apparently successful applications have appeared in the literature. What follows is only a selection of these applications, focusing on applications relevant to drug discovery, categorized by the type of receptor being studied. [Pg.448]

Transporters are the final class of integral membrane proteins that are important as drug targets. Structurally, they are among the most complex receptors known hence, they are ideally suited to analysis via pharmacophore discovery. Catalyst/HypoGen was used in the discovery of a pharmacophore for the Na + /bile acid transporter [61], A... [Pg.449]


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See also in sourсe #XX -- [ Pg.437 , Pg.455 ]




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