Biomedical subjects

Tomasz Magdziarz

Publications and source records attributed to Tomasz Magdziarz.

4 recordsLinked to original sources

PubMed2005

3D QSAR study of hypolipidemic asarones by comparative molecular surface analysis.

Three-dimensional quantitative structure-activity relationship (3D QSAR) modeled for alpha-asarone derivatives using the comparative molecular surface analysis (CoMSA) allowed us to reveal a correlation between the activity of these compounds and the electrostatic potential at the molecular surface. The grid formalism (s-CoMSA) allowed us to indicate a pharmacophore that is of key importance for compound activity. The CoMSA formalism coupled with the iterative variable elimination method gives a highly predictive model.

Allylbenzene Derivatives↗

PubMedDate not supplied

GRID formalism for the comparative molecular surface analysis: application to the CoMFA benchmark steroids, azo dyes, and HEPT derivatives.

Shape analysis is a powerful tool in chemistry and drug design, and molecular surface defines shape in the molecular scale. In the current publication we presented a novel formalism for the comparative molecular surface analysis (s-CoMSA). The method enables both quantitative modeling of 3D-QSAR and finding possible pharmacophoric sites. The method provides very predictive models for the CBG activity of the benchmark steroid series, tinctorial properties of the heterocyclic azo dyes and anti-HIV activity of the HEPT series.

Anti-HIV Agents↗

PubMedDate not supplied

Modeling robust QSAR. 1. Coding molecules in 3D-QSAR--from a point to surface sectors and molecular volumes.

Shape analysis is a powerful tool in chemistry and drug design. In the current work, we compare the results of CoMFA and Comparative Molecular Surface Analysis (CoMSA), the 3D-QSAR method, for a series of hypolipidemic and antiplatelet asarones and antifungal N-myristoyltransferase inhibitors. In this publication we show that a sector CoMSA formalism enables an analysis of the biological activity that is more directly related to the molecular shape and individual molecular functionalities than the traditional uniform and directionless CoMFA field. Iterative Variable Elimination allowed us to identify the potential pharmacophoric sites. We modeled QSARs for both series and demonstrate that sector-based molecular descriptors give very predictive models and allow one to generate a spatial interpretation of the QSAR models. In particular, we identified the central aromatic ring and carbonyl functions as the moieties determining the activity of the asarones series, while the pattern of substitution of the aromatic ring determines the activity of N-myristoyltransferase inhibitors.

Acyltransferases↗

PubMedDate not supplied

Modeling robust QSAR.

Quantitative Structure Activity Relationship (QSAR) is a term describing a variety of approaches that are of substantial interest for chemistry. This method can be defined as indirect molecular design by the iterative sampling of the chemical compounds space to optimize a certain property and thus indirectly design the molecular structure having this property. However, modeling the interactions of chemical molecules in biological systems provides highly noisy data, which make predictions a roulette risk. In this paper we briefly review the origins for this noise, particularly in multidimensional QSAR. This was classified as the data, superimposition, molecular similarity, conformational, and molecular recognition noise. We also indicated possible robust answers that can improve modeling and predictive ability of QSAR, especially the self-organizing mapping of molecular objects, in particular, the molecular surfaces, a method that was brought into chemistry by Gasteiger and Zupan.

Algorithms↗