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Structure Activity Relationships (structure + activity_relationships)
Selected AbstractsQSAR of Human Steroid 5,-Reductase Inhibitors: Where are the differences between isoenzyme type 1 and 2?MOLECULAR INFORMATICS, Issue 6 2004Michael Abstract Quantitative Structure Activity Relationships have been established for inhibitors of human steroid 5,-reductase including 6-azasteroids and non-steroidal compounds. From the applied descriptors, those related to the molecular geometry, electronic properties, and the electrostatic surface were derived from semi-empirical AM1 calculations. A chemical reaction as part of the inhibitory action is indicated by the presence of the ionization potential in the descriptor space. Strong similarities between the variables for the prediction of the binding affinity to the type 1 and IC50 values for the type 2 isoform of the 5,-reductase were observed. The most pronounced differences in the linear regression QSAR equations were found for the descriptors accounting for the hydrogen-bonding interaction, suggesting a different hydrogen-bonding pattern in the binding pocket of both isoforms. Furthermore, the topological indices together with the surface related descriptors point towards a lower content of aromatic amino acids in the binding site of the type 2 isoenzyme. Consequences for the design of new inhibitors are discussed. [source] Ortho Effects in Quantitative Structure Activity Relationships for Lipase Inhibition by Aryl CarbamatesMOLECULAR INFORMATICS, Issue 8 2003Gialih Lin Abstract Ortho -substituted phenyl- N -butyl carbamates (1,11) are synthesized and evaluated for their inhibition effects on Pseudomonas species lipase. Carbamates 1,11 are characterized as pseudo-substrate inhibitors of the enzyme. The logarithms of dissociation constant (Ki), carbamylation constant (k2), and bimolecular inhibition constant (ki) multiply linearly correlate with Hammett substituent constant (,), Taft-Kutter-Hansch ortho steric constant (ES), and Swan-Lupton field constant (F). For ,logKi -, logk2 -, and logki -correlations, values of ,, ,, f, ,XR are 0.2, ,0.06, ,1.7, 0.8; 0.0, 0.0, 1.0, ,0.07; and ,1.8, 7, 0.6, 5; respectively. The enzyme inhibition mechanism is composed of four steps: 1) the first step which is protonation of carbamates 1,11, 2) the second step (Ki1) which involves in the proton 1,3-shift of protonated carbamates 1,11 then the pseudo- trans to cis conformational change, 3) the third step (Ki2) which is formation of a negative charged enzyme-inhibitor tetrahedral intermediate, and 4) the fourth step (k2) which is the carbamylation step. The former three steps are likely composed of the Ki step. There is little ortho steric enhancement effect in the Ki step. From cross-interaction correlations, distance between carbamate and phenyl substituents in transition state for the Ki step is relatively short due to a large ,XR value of 7. The k2 step is insensitive to ortho steric effect. The k2 step involves in departure of leaving group, substituted phenol in which is protonated from the proton 1,3-shift but not from the active site histidine of the enzyme. From cross-interaction correlations, the distance between carbamate and phenyl substituents in transition state for the k2 step is relatively long due to a small ,XR value of 0.6. [source] Improving Opportunities for Regulatory Acceptance of QSARs: The Importance of Model Domain, Uncertainty, Validity and PredictabilityMOLECULAR INFORMATICS, Issue 3 2003Abstract For Quantitative Structure Activity Relationships (QSARs) to be accepted by the regulated and regulatory communities, their scope for use needs to be agreed upon by government and industry. This paper discusses the importance of model domain, uncertainty, validity and predictability assessment in promoting the regulatory acceptance of QSARs. [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] | ||||||||||