Author
Snezana Agatonovic-Kustrin
Other affiliations: Universiti Teknologi MARA, Monash University Malaysia Campus, University of South Australia ...read more
Bio: Snezana Agatonovic-Kustrin is an academic researcher from I.M. Sechenov First Moscow State Medical University. The author has contributed to research in topics: Molecular descriptor & DPPH. The author has an hindex of 28, co-authored 118 publications receiving 2857 citations. Previous affiliations of Snezana Agatonovic-Kustrin include Universiti Teknologi MARA & Monash University Malaysia Campus.
Papers published on a yearly basis
Papers
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TL;DR: Artificial neural networks are biologically inspired computer programs designed to simulate the way in which the human brain processes information and represent a promising modeling technique, especially for data sets having non-linear relationships which are frequently encountered in pharmaceutical processes.
1,144 citations
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TL;DR: A review of recent examples of the MTDL approach and fragment based strategy in the rational design of new potential AD medications highlights recent examples.
139 citations
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TL;DR: The structural characteristics and significance of functional groups as they relate to phytoestrogen selectivity for ER binding are explored.
128 citations
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TL;DR: 1-butanol, 1,2-hexanediol and 1, 2-octanediol produced balanced MEs capable of solubilising a high percentage of both oil and water and a similarity was observed between the descriptors attributed to 1- butanol and 1-2- hexanediol.
109 citations
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TL;DR: Results obtained indicate that lipophilicity, conformational stability and inter-molecular interactions (polarity, and hydrogen bonding) have the largest impact on intestinal absorption.
65 citations
Cited by
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TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.
Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …
33,785 citations
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TL;DR: A review about the application of response surface methodology (RSM) in the optimization of analytical methods is presented and the theoretical principles and steps for its application are described to introduce readers to this multivariate statistical technique.
4,338 citations
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TL;DR: The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems and proposed feedforwardand feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance.
1,471 citations
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TL;DR: Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades and theory behind the most important methods and recent successful applications are discussed.
Abstract: Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.
1,362 citations
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TL;DR: In this paper, the authors describe how in silico approaches will further increase our ability to predict and model the most relevant pharmacokinetic, metabolic and toxicity endpoints, thereby accelerating the drug discovery process.
Abstract: Following studies in the late 1990s that indicated that poor pharmacokinetics and toxicity were important causes of costly late-stage failures in drug development, it has become widely appreciated that these areas should be considered as early as possible in the drug discovery process. However, in recent years, combinatorial chemistry and high-throughput screening have significantly increased the number of compounds for which early data on absorption, distribution, metabolism, excretion (ADME) and toxicity (T) are needed, which has in turn driven the development of a variety of medium and high-throughput in vitro ADMET screens. Here, we describe how in silico approaches will further increase our ability to predict and model the most relevant pharmacokinetic, metabolic and toxicity endpoints, thereby accelerating the drug discovery process.
1,330 citations