Get 3D QSAR in Drug Design: Ligand-Protein Interactions and PDF

By Tommy Liljefors (auth.), Hugo Kubinyi, Gerd Folkers, Yvonne C. Martin (eds.)

ISBN-10: 0306468573

ISBN-13: 9780306468575

ISBN-10: 0792347900

ISBN-13: 9780792347903

Significant development has been made within the examine of 3-dimensional quantitative structure-activity relationships (3D QSAR) because the first ebook via Richard Cramer in 1988 and the 1st quantity within the sequence. 3D QSAR in Drug layout. concept, tools and purposes, released in 1993. the purpose of that early e-book was once to give a contribution to the knowledge and the additional software of CoMFA and comparable ways and to facilitate the proper use of those equipment. due to the fact then, enormous quantities of papers have seemed utilizing the fast constructing suggestions of either 3D QSAR and computational sciences to check a huge number of organic difficulties. back the editor(s) felt that the time had come to solicit studies on released and new viewpoints to rfile the state-of-the-art of 3D QSAR in its broadest definition and to supply visions of the place new options will emerge or new appli- tions will be came upon. The goal is not just to focus on new rules but additionally to teach the shortcomings, inaccuracies, and abuses of the tools. we are hoping this ebook will allow others to split trivial from visionary methods and me-too method from in- vative suggestions. those matters guided our number of individuals. To our pride, our demand papers elicited an excellent many manuscripts.

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Extra info for 3D QSAR in Drug Design: Ligand-Protein Interactions and Molecular Similarity

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Analysis of the importance of different energetic terms and the effects of different chemometric protocols has shown how robust models can be obtained. Further work should include the exploration of the effects of including additional descriptors such as those that explicitly describe entropic changes on binding. In addition, it may be possible to obtain improvements in the method by optimizing modelling protocols and tailoring the chemometric tools more specifically towards the data extracted from the molecular systems studied.

In this work [46]. the probabilities of observing a particular distance were computed. normalized by the frequencies observed for all types of L–R atom pairs in the training set. then translated into mean force potentials. To minimize the effect of low-occurrence data points in the parametcrization, a cross-validation procedure was applied by eliminating the evaluated complex from the training set for the empirical calculations of the seven studied HIV-1 protease inhibitor complexes [46]. The authors further break down the contributions of various terms from their master equation (Eq.

Whereas DOCK, a cavity-matching algorithm [8–11] has been quite successful in finding non-congeneric molecules of the correct shape to interact with a receptor [12,13]. Caflisch et al. [14] have used a fragmentbased approach to map high-affinity sites on HIV-1 protease [15] with the idea of * T o whom correspondence should be addressed. H. Kubinyi et al. ), 3D QSAR in Drug Design, Volume 2 35-61. © 1998 Kluwer Academic Publishers, Printed in Great Britan. Tudor I. Oprea and Garland R. Marshall Fig.

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3D QSAR in Drug Design: Ligand-Protein Interactions and Molecular Similarity by Tommy Liljefors (auth.), Hugo Kubinyi, Gerd Folkers, Yvonne C. Martin (eds.)


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