Andrzej Bak

Bio: Andrzej Bak is an academic researcher from University of Silesia in Katowice. The author has contributed to research in topics: Quantitative structure–activity relationship & Pharmacophore. The author has an hindex of 14, co-authored 54 publications receiving 635 citations. Previous affiliations of Andrzej Bak include Silesian University.

Modeling robust QSAR.

Abstract: 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.

Modeling robust QSAR 3: SOM-4D-QSAR with iterative variable elimination IVE-PLS: application to steroid, azo dye, and benzoic acid series.

Abstract: In the current paper we present a receptor-independent 4D-QSAR method based on self-organizing mapping (SOM-4D-QSAR) and in particular focus on its pharmacophore mapping ability. We use a novel stochastic procedure to verify the predictive ability of the method for a large population of 4D-QSAR models generated. This systematic study was conducted on a series of benzoic acids, azo dyes, and steroids that bind aromatase. We show that the 4D-QSAR method coupled with IVE-PLS provides a very stable and predictive modeling technique. The method enables us to identify the molecular motifs contributing the most to the fiber-dye affinity and the aromatase enzyme binding activity of the steroid. However, the method appeared much less effective for the benzoic acid series, in which the efficacy was limited by electronic effects strictly correlated to a single conformer.

The Comparative Molecular Surface Analysis (COMSA) − A Nongrid 3D QSAR Method by a Coupled Neural Network and PLS System: Predicting pKa Values of Benzoic and Alkanoic Acids

Abstract: A self-organizing neural network was used to design a novel method capable of the quantitative prediction of molecular properties. The method is based on the comparison of molecular surfaces performed by the coupled neural network and PLS system. Unlike CoMFA and related methods it does not compare the properties describing a discrete set of points but the average property values calculated for a certain area of the molecular surface. It has been found that the results of the PLS analysis of the series of the comparative matrices of the molecular electrostatic potential (MEP) are quite stable. Also the results only slightly depend on such parameters as the number of points sampled at the molecular surface (D) or a winning distance (MD) of the self-organizing neurons. The influence of these parameters for modeling the effects limited by steric and electronic effects was determined and the pKa values of the ortho-, meta-, and para- (o-, m-, p-) analogues of benzoic acid and selected alkanoic acids were pred...

Sulfolane: Magic Extractor or Bad Actor? Pilot-Scale Study on Solvent Corrosion Potential

Abstract: The sulfur-containing derivatives and their metabolites, regarded as ‘old devils of green’ chemistry, constitute a relevant class of air/water/soil contaminants in over-polluted world. In fact, some industrially-engineered solvents have become environmentally unfavorable. An attractive alternative to commonly used industrial liquids is sulfolane (C4H8SO2), an anthropogenic medium. The main objective of this paper is the comprehensive review focusing mainly on the state-of-the-art aspects of the sulfolane synthesis, application of sulfolane as an extractive solvent due to its ‘unique’ physicochemical properties as well as the potential of sulfolane to cause equipment corrosion and subsequent spills. The potential risk for groundwater contamination, danger for human health and ways of sulfolane biodegradation were briefly reviewed as well. Interestingly, the analysis performed on data stored in the Reaxys database revealed an alternating tendency of waxing and waning interest in sulfolane during the space of the last fifty years. Moreover, the primary goal of the presented case study was to verify applicability of industrial, multi-electrochemical technique for reliable detection of corrosion in low conductive process fluids. Several aspects of corrosion measurement including the impact of process parameters (temperature) and impurities (oxygen and chlorides) on stainless steel corrosion in pure sulfolane were investigated briefly.

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The Self-Organizing Map

Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

国际刊物《Scientometrics》文献计量研究

Abstract: 本文对国际科学计量学杂志《Ｓｃｉｅｎｔｏｍｅｔｒｉｃｓ》１９７９－１９９１年的研究论文内容、栏目、作者及国别和编委及国别作了计量分析，揭示出科学计量学研究的重点、活动的中心及发展趋势，说明了学科带头人在发展科学计量学这门新兴学科中的作用。

3D-QSAR in drug design--a review.

Abstract: Quantitative structure-activity relationships (QSAR) have been applied for decades in the development of relationships between physicochemical properties of chemical substances and their biological activities to obtain a reliable statistical model for prediction of the activities of new chemical entities. The fundamental principle underlying the formalism is that the difference in structural properties is responsible for the variations in biological activities of the compounds. In the classical QSAR studies, affinities of ligands to their binding sites, inhibition constants, rate constants, and other biological end points, with atomic, group or molecular properties such as lipophilicity, polarizability, electronic and steric properties (Hansch analysis) or with certain structural features (Free-Wilson analysis) have been correlated. However such an approach has only a limited utility for designing a new molecule due to the lack of consideration of the 3D structure of the molecules. 3D-QSAR has emerged as a natural extension to the classical Hansch and Free-Wilson approaches, which exploits the three-dimensional properties of the ligands to predict their biological activities using robust chemometric techniques such as PLS, G/PLS, ANN etc. It has served as a valuable predictive tool in the design of pharmaceuticals and agrochemicals. Although the trial and error factor involved in the development of a new drug cannot be ignored completely, QSAR certainly decreases the number of compounds to be synthesized by facilitating the selection of the most promising candidates. Several success stories of QSAR have attracted the medicinal chemists to investigate the relationships of structural properties with biological activity. This review seeks to provide a birds eye view of the different 3D-QSAR approaches employed within the current drug discovery community to construct predictive structure- activity relationships and also discusses the limitations that are fundamental to these approaches, as well as those that might be overcome with the improved strategies. The components involved in building a useful 3D-QSAR model are discussed, including the validation techniques available for this purpose.

In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling

Abstract: Pharmacology over the past 100 years has had a rich tradition of scientists with the ability to form qualitative or semi-quantitative relations between molecular structure and activity in cerebro. To test these hypotheses they have consistently used traditional pharmacology tools such as in vivo and in vitro models. Increasingly over the last decade however we have seen that computational (in silico) methods have been developed and applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, pharmacophores, homology models and other molecular modeling approaches, machine learning, data mining, network analysis tools and data analysis tools that use a computer. In silico methods are primarily used alongside the generation of in vitro data both to create the model and to test it. Such models have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The aim of this review is to illustrate some of the in silico methods for pharmacology that are used in drug discovery. Further applications of these methods to specific targets and their limitations will be discussed in the second accompanying part of this review.