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Molecular Interaction Fields (molecular + interaction_field)
Selected AbstractsMusings on ADME Predictions and Structure,Activity RelationsCHEMISTRY & BIODIVERSITY, Issue 11 2005Bernard Testa The first part of the paper examines Structure,Activity Relations (SARs) and their components from a very general point of view. The various types of interpretation emerging from statistically valid relations will be examined, namely causal (mechanistic), contextual (empirical), fortuitous, and tautological correlations. Implications for ADME predictions will be seen when discussing the diversity of interactions between active compounds (e.g., drugs) and biological systems. The second part of the paper is more specific and presents the concept of molecular-property space, an all but neglected concept in SARs. Recent results from Molecular Dynamics (MD) simulations and Molecular Interaction Fields (MIF) computations of acetylcholine will be used to illustrate not only the well-known conformational space of this molecule, but also its property space as exemplified by its lipophilicity space. It will be seen that a molecule as small as acetylcholine is able to span a relatively broad property space. Most significantly in an ADME perspective, the molecule is able, within the limits of its property space, to adapt to the medium. This is equivalent to saying that the medium constrains the molecule to resemble it as much as feasible. [source] A Three-Dimensional Quanititative Structure-Activity Relationship (3D-QSAR) Model for Predicting the Enantioselectivity of Candida antarctica Lipase BADVANCED SYNTHESIS & CATALYSIS (PREVIOUSLY: JOURNAL FUER PRAKTISCHE CHEMIE), Issue 9 2009Paolo Braiuca Abstract Computational techniques involving molecular modeling coupled with multivariate statistical analysis were used to evaluate and predict quantitatively the enantioselectivity of lipase B from Candida antarctica (CALB). In order to allow the mathematical and statistical processing of the experimental data largely available in the literature (namely enantiomeric ratio E), a novel class of GRID-based molecular descriptors was developed (differential molecular interaction fields or DMIFs). These descriptors proved to be efficient in providing the structural information needed for computing the regression model. Multivariate statistical methods based on PLS (partial least square , projection to latent structures), were used for the analysis of data available from the literature and for the construction of the first three-dimensional quanititative structure-activity relationship (3D-QSAR) model able to predict the enantioselectivity of CALB. Our results indicate that the model is statistically robust and predictive. [source] An alignment-free methodology for modelling field-based 3D-structure activity relationships using inductive logic programmingJOURNAL OF CHEMOMETRICS, Issue 12 2007Bård Buttingsrud Abstract Traditional 3D-quantitative structure,activity relationship (QSAR)/structure,activity relationship (SAR) methodologies are sensitive to the quality of an alignment step which is required to make molecular structures comparable. Even though many methods have been proposed to solve this problem, they often result in a loss of model interpretability. The requirement of alignment is a restriction imposed by traditional regression methods due to their failure to represent relations between data objects directly. Inductive logic programming (ILP) is a class of machine-learning methods able to describe relational data directly. We propose a new methodology which is aimed at using the richness in molecular interaction fields (MIFs) without being restricted by any alignment procedure. A set of MIFs is computed and further compressed by finding their minima corresponding to the sites of strongest interaction between a molecule and the applied test probe. ILP uses these minima to build easily interpretable rules about activity expressed as pharmacophore rules in the powerful language of first-order logic. We use a set of previously published inhibitors of factor Xa of the benzamidine family to discuss the problems, requirements and advantages of the new methodology. Copyright © 2007 John Wiley & Sons, Ltd. [source] Comparative Investigation of the ATP-Binding Site of Human and Plasmodial Glycogen Synthase Kinase-3MOLECULAR INFORMATICS, Issue 8 2009Sebastian Kruggel Abstract Malaria is still one of the most problematic infectious diseases besides AIDS and tuberculosis. Plasmodial glycogen synthase kinase-3 (PfGSK-3) has been proposed as a potential malaria target before but the plasmodial enzyme is not crystallized yet whereas there are several structures published of the human glycogen synthase kinase-3 (HsGSK-3). Here we describe the comparison of different PDB structures of the HsGSK-3 and corresponding homology models of PfGSK-3. The differences were investigated with molecular interaction fields and also by a docking study of the known inhibitors kenpaullone and flavopiridol. [source] Combining Computational and Biochemical Studies for a Rationale on the Anti-Aromatase Activity of Natural PolyphenolsCHEMMEDCHEM, Issue 12 2007Marco Abstract Aromatase, an enzyme of the cytochrome,P450 family, is a very important pharmacological target, particularly for the treatment of breast cancer. The anti-aromatase activity of a set of natural polyphenolic compounds was evaluated in,vitro. Strong aromatase inhibitors including flavones, flavanones, resveratrol, and oleuropein, with activities comparable to that of the reference anti-aromatase drug aminoglutethimide, were identified. Through the application of molecular modeling techniques based on grid-independent descriptors and molecular interaction fields, the major physicochemical features associated with inhibitory activity were disclosed, and a putative virtual active site of aromatase was proposed. Docking of the inhibitors into a 3D homology model structure of the enzyme defined a common binding mode for the small molecules under investigation. The good correlation between computational and biological results provides the first rationalization of the anti-aromatase activity of polyphenolic compounds. Moreover, the information generated in this approach should be further exploited for the design of new aromatase inhibitors. [source] | ||||||||||