3D QSAR in Drug Design: Ligand-Protein Interactions and by Hugo Kubinyi, Gerd Folkers, Yvonne C. Martin

By Hugo Kubinyi, Gerd Folkers, Yvonne C. Martin

Major development has been made within the learn of 3-dimensional quantitative structure-activity relationships (3D QSAR) because the first book by means of Richard Cramer in 1988 and the 1st quantity within the sequence. 3D QSAR in Drug layout. concept, tools and functions, released in 1993. the purpose of that early ebook used to be to give a contribution to the certainty and the extra program of CoMFA and comparable techniques and to facilitate the right use of those equipment. on account that then, enormous quantities of papers have seemed utilizing the speedy constructing thoughts of either 3D QSAR and computational sciences to review a large number of organic difficulties. back the editor(s) felt that the time had come to solicit experiences 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 suggestions will emerge or new appli- tions should be stumbled on. The purpose isn't just to spotlight new rules but in addition to teach the shortcomings, inaccuracies, and abuses of the tools. we are hoping this e-book will permit others to split trivial from visionary methods and me-too technique from in- vative strategies. those matters guided our collection of individuals. To our pride, our demand papers elicited an excellent many manuscripts.

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Extra resources for 3D QSAR in Drug Design: Ligand-Protein Interactions and Molecular Similarity, Vol. 2

Example text

Secondly, it is realized that binding free energy is rarely a linear function of binding energy. The extensive decomposition allows those components that are predictive of binding free energy to be detected and these may implicitly represent other physically important interactions or even entropic terms. 20 Comparative Binding Energy Analysis A QSAR model is derived for each target receptor studied with the COMBINE method, as the method was specifically designed for ligand optimization. Thus, a derived regression model is not applicable to all ligand-receptor interactions in the way that a general-purpose empirical ‘scoring function’ derived from statistical analysis of a diverse set of protein-ligand complexes is designed to be [17,18].

Manuel Pastor for helpful comments on chemometrics and provision of the GOLPE program; Dr. Albert Palomer for bringing the problem of structure–activity relationships of PLA2 inhibitors to our attention: Dr. Mayte Pisabarro for her contribution to the modelling of the PLA2 inhibitors; Dr. Kate Holloway for provision of cartesian coordinates for the training set of inhibitors and L-689, 502-bound HIV-1 proteinase; and Dr. J. Kraulis for the MOLSCRIPT program. O. was the recipient of a predoctoral fellowship from the Comunidad Autonoma de Madrid.

The phospholipase A2 example. on the other hand, can be regarded as a particularly difficult case. in the sense that the initial correlation between experimental activities and calculated binding energies was rather poor. The high correlation between calculated intermolecular interaction energies (using the MM2X force field) and enzyme inhibition reported for a set of 33 HlV-1 proteinase inhibitors [14] prompted us to apply the COMBINE methodology to this same data set using the AMBER force field [24].

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