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Alexander V. Dmitriev

Bio: Alexander V. Dmitriev is an academic researcher from Institute of Business & Medical Careers. The author has contributed to research in topics: Quantitative structure–activity relationship & Metabolite. The author has an hindex of 11, co-authored 32 publications receiving 504 citations. Previous affiliations of Alexander V. Dmitriev include Russian National Research Medical University & Russian Academy.

Papers
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Journal ArticleDOI
TL;DR: This review contains a practical example of the application of combined chemo- and bioinformatics methods to study pleiotropic therapeutic effects of 50 medicinal plants from Traditional Indian Medicine.

102 citations

Journal ArticleDOI
TL;DR: A new freely available web server site of metabolism predictor to predict the sites of metabolism (SOM) based on the structural formula of chemicals has been developed using a Bayesian approach and labelled multilevel neighbourhoods of atoms descriptors to represent the structures of over 1000 metabolized xenobiotics.
Abstract: UNLABELLED A new freely available web server site of metabolism predictor to predict the sites of metabolism (SOM) based on the structural formula of chemicals has been developed. It is based on the analyses of 'structure-SOM' relationships using a Bayesian approach and labelled multilevel neighbourhoods of atoms descriptors to represent the structures of over 1000 metabolized xenobiotics. The server allows predicting SOMs that are catalysed by 1A2, 2C9, 2C19, 2D6 and 3A4 isoforms of cytochrome P450 and enzymes of the UDP-glucuronosyltransferase family. The average invariant accuracy of prediction that was calculated for the training sets (using leave-one-out cross-validation) and evaluation sets is 0.9 and 0.95, respectively. AVAILABILITY AND IMPLEMENTATION Freely available on the web at http://www.way2drug.com/SOMP.

70 citations

Journal ArticleDOI
TL;DR: This work generated virtually all possible binary combinations of marketed drugs and employed QSAR models to identify drug pairs predicted to be instances of DDI, and 4500 of these predicted DDIs that were not found in the training sets were confirmed by data from the DrugBank database.
Abstract: Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100 000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug–drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27 966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair o...

49 citations

Journal ArticleDOI
TL;DR: The current version of the PASS program for prediction of biological activity spectra of organic compounds based on analysis of structure—activity relationships (SAR) for a training set containing information on more than 1000 000 biologically active organic compounds is described.
Abstract: We describe the current version of the PASS program for prediction of biological activity spectra of organic compounds based on analysis of structure—activity relationships (SAR) for a training set containing information on more than 1000 000 biologically active organic compounds. The average accuracy of prediction for more than 5 000 types of biological activity exceeds a value of 0.96. To analyze quantitative SAR, the GUSAR program was developed. The advantages of GUSAR were demonstrated in a number of computational experiments. The local versions of the PASS and GUSAR programs, as well as 19 freely available web services were developed. The latter are freely accessible via the Internet at http://way2drug.com/dr. The web services at the Way2Drug portal are used by more than 24 000 researchers working in about 100 countries. Currently, more than 830 000 predictions were made, the most promising compounds were selected for chemical synthesis, and priorities for testing their biological activity were established. The PharmaExpert software was developed to analyze the results of the PASS- and GUSAR-based predictions and to search for chemical compounds with necessary biological activity spectra. Combined use of the PASS, GUSAR, and PharmaExpert programs enables an assessment of the pharmacotherapeutic, adverse, and toxic effects of new compounds based on the systems pharmacology.

48 citations

Journal ArticleDOI
TL;DR: A new ligand-based method for the prediction of sites of metabolism (SOMs) for xenobiotics has been developed on the basis of the LMNA (labeled multilevel neighborhoods of atom) descriptors and the PASS (prediction of activity spectra for substances) algorithm and applied to predict the SOMs of the 1A2, 2C9,2C19, 2D6, and 3A4 isoforms of cytochrome P450.
Abstract: A new ligand-based method for the prediction of sites of metabolism (SOMs) for xenobiotics has been developed on the basis of the LMNA (labeled multilevel neighborhoods of atom) descriptors and the PASS (prediction of activity spectra for substances) algorithm and applied to predict the SOMs of the 1A2, 2C9, 2C19, 2D6, and 3A4 isoforms of cytochrome P450. An average IAP (invariant accuracy of prediction) of SOMs calculated by the leave-one-out cross-validation procedure was 0.89 for the developed method. The external validation was made with evaluation sets containing data on biotransformations for 57 cardiovascular drugs. An average IAP of regioselectivity for evaluation sets was 0.83. It was shown that the proposed method exceeds accuracy of SOM prediction by RS-Predictor for CYP 1A2, 2D6, 2C9, 2C19, and 3A4 and is comparable to or better than SMARTCyp for CYP 2C9 and 2D6.

47 citations


Cited by
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01 Feb 1995
TL;DR: In this paper, the unpolarized absorption and circular dichroism spectra of the fundamental vibrational transitions of the chiral molecule, 4-methyl-2-oxetanone, are calculated ab initio using DFT, MP2, and SCF methodologies and a 5S4P2D/3S2P (TZ2P) basis set.
Abstract: : The unpolarized absorption and circular dichroism spectra of the fundamental vibrational transitions of the chiral molecule, 4-methyl-2-oxetanone, are calculated ab initio. Harmonic force fields are obtained using Density Functional Theory (DFT), MP2, and SCF methodologies and a 5S4P2D/3S2P (TZ2P) basis set. DFT calculations use the Local Spin Density Approximation (LSDA), BLYP, and Becke3LYP (B3LYP) density functionals. Mid-IR spectra predicted using LSDA, BLYP, and B3LYP force fields are of significantly different quality, the B3LYP force field yielding spectra in clearly superior, and overall excellent, agreement with experiment. The MP2 force field yields spectra in slightly worse agreement with experiment than the B3LYP force field. The SCF force field yields spectra in poor agreement with experiment.The basis set dependence of B3LYP force fields is also explored: the 6-31G* and TZ2P basis sets give very similar results while the 3-21G basis set yields spectra in substantially worse agreements with experiment. jg

1,652 citations

Journal ArticleDOI
TL;DR: This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed inQSAR to a wide range of research areas outside of traditional QSar boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics.
Abstract: Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure–activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.

383 citations

Journal ArticleDOI
TL;DR: This review focuses on the diverse effects and efficacy of herbal compounds in controlling the development of MDR in microbes and hopes to inspire research into unexplored plants with a view to identify novel antibiotics for global health benefits.
Abstract: The war on multidrug resistance (MDR) has resulted in the greatest loss to the world’s economy. Antibiotics, the bedrock, and wonder drug of the 20th century have played a central role in treating infectious diseases. However, the inappropriate, irregular, and irrational uses of antibiotics have resulted in the emergence of antimicrobial resistance. This has resulted in an increased interest in medicinal plants since 30–50% of current pharmaceuticals and nutraceuticals are plant-derived. The question we address in this review is whether plants, which produce a rich diversity of secondary metabolites, may provide novel antibiotics to tackle MDR microbes and novel chemosensitizers to reclaim currently used antibiotics that have been rendered ineffective by the MDR microbes. Plants synthesize secondary metabolites and phytochemicals and have great potential to act as therapeutics. The main focus of this mini-review is to highlight the potential benefits of plant derived multiple compounds and the importance of phytochemicals for the development of biocompatible therapeutics. In addition, this review focuses on the diverse effects and efficacy of herbal compounds in controlling the development of MDR in microbes and hopes to inspire research into unexplored plants with a view to identify novel antibiotics for global health benefits.

301 citations

Journal ArticleDOI
TL;DR: It is argued that a network pharmacology approach would enable an effective mapping of the yet unexplored target space of natural products, hence providing a systematic means to extend the druggable space of proteins implicated in various complex diseases.

285 citations