By Yvonne Connolly Martin (auth.), Hugo Kubinyi, Gerd Folkers, Yvonne C. Martin (eds.)
Significant development has been made within the examine of 3-dimensional quantitative structure-activity relationships (3D QSAR) because the first booklet by way of Richard Cramer in 1988 and the 1st quantity within the sequence, 3D QSAR in Drug layout. conception, tools and purposes, released in 1993. the purpose of that early ebook was once to give a contribution to the certainty and the extra software of CoMFA and similar techniques and to facilitate the suitable use of those equipment. seeing that then, 1000s of papers have seemed utilizing the fast constructing suggestions of either 3D QSAR and computational sciences to review a vast number of organic difficulties. back the editor(s) felt that the time had come to solicit experiences on released and new viewpoints to record the state-of-the-art of 3D QSAR in its broadest definition and to supply visions of the place new recommendations will emerge or new appli- tions can be chanced on. The purpose isn't just to focus on new principles but in addition to teach the shortcomings, inaccuracies, and abuses of the equipment. we are hoping this publication will permit others to split trivial from visionary techniques and me-too technique from in- vative suggestions. those issues guided our collection of individuals. To our pride, our demand papers elicited a very good many manuscripts.
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Extra info for 3D QSAR in Drug Design: Recent Advances
Computer lead finding and optimization: Proceedings of the 11th European Symposium on Structure-Activity Relationships, Wiley-VCH, Basel, Switzerland, 1977, pp. 379–395. , Cruciani, G. , Smart Region Definition SRD: A new way to improve the predictive ability and interpretability of three-dimensional quantitative structure–activity relationships, J. Med. , 40 (1997) 1455–1464. 7 1 . -C. 3. Three-dimensional quantitative structure-activity relationship study of 4'-O-demethylepipodophyllotoxin 2 analogs using the modified CoMFA/q -GRS approach, J.
2. , Smee, D. , Analysis of the in vitro activity of certain ribonucieosides against puruinfluenza virus using a novel computer-aided molecular modeling procedure, J. Med. , 32 (1989) 746–756. 3. E. , Comparative molecular field analysis (CoMFA): 1. Effect of shape on binding of steroids to carrier proteins, J. Am. Chem. , 110 (1988) 5959–5967. 4. , A PLS QSAR analysis using 3D generated aromatic descriptors of principal property type: Application to some dopamine D2 benzamide antagonists, J. -Aided Mol.
Quant. -Act. , 13 (1994) 393–401. 130. Rogers, D. , Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships, J. Chem. Inf. Comput. , 34 (1994) 854–866. 1 3 1 . , Sjostrum, M. , Interactive variable selection (IVS) for PLS: 2. Chemical applications, J. Chemometrics, 9 (1995) 331 –342. 22 3D QSAR: Current State, Scope, and Limitations 132. , Villa, A. , Neural-network studies: 2. Variable selection, J. Chem. I n f .