3D QSAR in Drug Design: Ligand-Protein Interactions and - download pdf or read online

By Hugo Kubinyi, Gerd Folkers, Yvonne C. Martin

ISBN-10: 0306468573

ISBN-13: 9780306468575

ISBN-10: 0792347900

ISBN-13: 9780792347903

Volumes 2 and three of the 3D QSAR in Drug layout sequence target to check the development being made in CoMFA and different 3D QSAR ways because the book of the hugely winning first quantity approximately 4 years in the past. quantity 2 (Ligand-Protein Interactions and Molecular Similarity) divides into 3 sections facing Ligand-Protein Interactions, Quantum Chemical types and Molecular Dynamics Simulations, and Pharmacophore Modelling and Molecular Similarity, respectively. quantity three (Recent Advances) is usually divided into 3 sections, specifically 3D QSAR technique: CoMFA and similar ways, Receptor types and different 3D QSAR techniques, and 3D QSAR functions. greater than seventy extraordinary scientists have contributed approximately 40 stories in their paintings and comparable learn to those volumes that are of remarkable caliber and timeliness. those works current an up to date assurance of the most recent advancements in all fields of 3D QSAR.

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

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Med. , 37 (1995) 4118–4129. , Computational methods to predict binding free energy in ligand–receptor complexes, J. Med. , 38 (1995) 4952–4967. L. , The statistical thermodynamic basis for computation of binding affinites: a critical review, Biophys. , 72 (1997) 1047–1069. , Ligand-protein docking and rational drug design, Curr. Op. Str. Biol. 5 (1995) 224–228. , de Pascual Teresa, B. , Assessment of solvation effects on calculated binding affinity differences: Trypsin inhibition by flavonoids as a model system for congeneric series.

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.

Each inhibitor was considered as a single fragment and no intramolecular energy terms were considered. The number of variables per inhibitor was thus equal to 2 (van der Waals and electrostatic) times the number of protein residues ([2 × 99 amino acids] + 1 water molecule) = 398. No variable selection was employed. 1 kcal/mol. This pretreatment reduced the number of variables that entered the PLS analysis to around 50. It is noteworthy that the number of variables was effectively reduced in this example, without the need for variable selection, underscoring the fact that it is possible for a simple pretreatment of the original matrix to accomplish virtually the same effect.

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3D QSAR in Drug Design: Ligand-Protein Interactions and Molecular Similarity, Vol. 2 by Hugo Kubinyi, Gerd Folkers, Yvonne C. Martin

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