Biomedical subjects

Rafal Gieleciak

Publications and source records attributed to Rafal Gieleciak.

8 recordsLinked to original sources

PubMed2006

Comparative molecular surface analysis (CoMSA) for virtual combinatorial library screening of styrylquinoline HIV-1 blocking agents.

We used comparative molecular surface analysis to design molecules for the synthesis as part of the search for new HIV-1 integrase inhibitors. We analyzed the virtual combinatorial library (VCL) constituted from various moieties of styrylquinoline and styrylquinazoline inhibitors. Since imines can be applied in a strategy of dynamic combinatorial chemistry (DCC), we also tested similar compounds in which the -C=N- or -N=C- linker connected the heteroaromatic and aromatic moieties. We then used principal component analysis (PCA) or self-organizing maps (SOM), namely, the Kohonen neural networks to obtain a clustering plot analyzing the diversity of the VCL formed. Previously synthesized compounds of known activity, used as molecular probes, were projected onto this plot, which provided a set of promising virtual drugs. Moreover, we further modified the above mentioned VCL to include the single bond linker -C-N- or -N-C-. This allowed increasing compound stability but expanded also the diversity between the available molecular probes and virtual targets. The application of the CoMSA with SOM indicated important differences between such compounds and active molecular probes. We synthesized such compounds to verify the computational predictions.

Anti-HIV Agents↗

PubMed2004

Probability issues in molecular design: predictive and modeling ability in 3D-QSAR schemes.

In the current work we investigated 3D-QSAR data by the use of the coupled leave-several-out (LSO) and leave-one-out (LOO) cross-validation (CV) procedures. We verified the above mentioned scheme using both simulated data and real 3D QSAR data describing a series of CoMFA steroids, heterocyclic azo dyes and styrylquinoline HIV integrase inhibitors. Unlike in standard analyses, this technique characterizes individual method not by a single performance metrics but screens a whole possible modeling space by sampling different molecules into the training and test sets, respectively. This allowed us for the discussion of the information included in the estimators validating cross-validation procedures, as well as the comparison of the efficiency of several 3D QSAR schemes, in particular, Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Surface Analysis (CoMSA). Moreover, it allows one to acquire some general knowledge about predictive and modeling ability in 3D QSAR method.

Drug Design↗

PubMed2003

Comparative molecular surface analysis: a novel tool for drug design and molecular diversity studies.

The application of the SOM network in drug design and molecular diversity is discussed. In particular, examples of the applications of the Comparative Molecular Surface Analysis (CoMSA) are reviewed. Molecular surface is a fuzzy category, inspired by the macroscopic world, which has no unique equivalent in the molecular scale. However, it is somewhere near the area where the molecular recognition processes are taking place. Consequently, the methods that analyze this region promise better efficiency than procedures that are based on uniform grids. An important advantage of the CoMSA method is the possibility for the generation of fuzzy molecular representations together with its ability to discover such aspects of molecular similarity that can be easily overlooked by a chemist. The ability for data compression is a further advantage. It has also been shown that the fast processing of the comparative Kohonen mapping enables one to implement this method in the field of molecular diversity.

Anti-HIV Agents↗

PubMed2002

Use of the Kohonen neural network for rapid screening of ex vivo anti-HIV activity of styrylquinolines.

Using the Kohonen neural network, the electrostatic potentials on the molecular surfaces of 14 styrylquinoline derivatives were drawn as comparative two-dimensional maps and compared with their known human immunodeficiency virus (HIV)-1 replication blocking potency in cells. A feature of the potential map was discovered to be related with the HIV-1 blocking activity and was used to unmask the activity of further five analogues, previously described but whose cytotoxicity precluded an estimation of their activity, and to predict the activity of 10 new compounds while the experimental data were unknown. The measurements performed later turned out to agree with the predictions.

Anti-HIV Agents↗

PubMedDate not supplied

Comparative molecular surface analysis (CoMSA) for modeling dye-fiber affinities of the azo and anthraquinone dyes.

Despite recent investigations aimed at modeling 3D QSAR for dye molecules a controversy still exists: can a pharamacophore hypothesis be used for such purposes. In the present publication we reported on the application of the CoMSA method for modeling 3D QSAR of azo and anthraquinone dyes. We obtained very predictive models, which significantly outperform those reported in the previous CoMFA studies, especially for the azo dyes. Our results proved the previous conclusion that steric requirements are far less pronounced for the cellulose cavities than for the classical drug receptor. Moreover, our results indicate that all molecular surface segments are important for dye-fiber interactions, which also makes an important difference in relation to the classical drug pharmacophore. On the other hand, high predictivity of the CoMSA models indicates that a pharmacophore concept is suitable for the description of the dye-fiber interactions. However, this pharmacophore must substantially differ from the drug pharmacophore used for the illustration of the drug-receptor interactions. From a theoretical point of view dye-cellulose interactions can be an interesting case in which shape decides the activity rules not by the steric repulsion but as a cofactor determining the electrostatic potential distribution.

Journal Article↗

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↗