Dmitry S. Druzhilovskiy

Bio: Dmitry S. Druzhilovskiy is an academic researcher from Institute of Business & Medical Careers. The author has contributed to research in topics: Virtual screening & chEMBL. The author has an hindex of 9, co-authored 18 publications receiving 254 citations.

CLC-Pred: A freely available web-service for in silico prediction of human cell line cytotoxicity for drug-like compounds.

Abstract: In silico methods of phenotypic screening are necessary to reduce the time and cost of the experimental in vivo screening of anticancer agents through dozens of millions of natural and synthetic chemical compounds. We used the previously developed PASS (Prediction of Activity Spectra for Substances) algorithm to create and validate the classification SAR models for predicting the cytotoxicity of chemicals against different types of human cell lines using ChEMBL experimental data. A training set from 59,882 structures of compounds was created based on the experimental data (IG50, IC50, and % inhibition values) from ChEMBL. The average accuracy of prediction (AUC) calculated by leave-one-out and a 20-fold cross-validation procedure during the training was 0.930 and 0.927 for 278 cancer cell lines, respectively, and 0.948 and 0.947 for cytotoxicity prediction for 27 normal cell lines, respectively. Using the given SAR models, we developed a freely available web-service for cell-line cytotoxicity profile prediction (CLC-Pred: Cell-Line Cytotoxicity Predictor) based on the following structural formula: http://way2drug.com/Cell-line/.

Computer-aided prediction of biological activity spectra for organic compounds: the possibilities and limitations

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.

How good are publicly available web services that predict bioactivity profiles for drug repurposing

Abstract: Drug repurposing provides a non-laborious and less expensive way for finding new human medicines. Computational assessment of bioactivity profiles shed light on the hidden pharmacological potential of the launched drugs. Currently, several freely available computational tools are available via the Internet, which predict multitarget profiles of drug-like compounds. They are based on chemical similarity assessment (ChemProt, SuperPred, SEA, SwissTargetPrediction and TargetHunter) or machine learning methods (ChemProt and PASS). To compare their performance, this study has created two evaluation sets, consisting of (1) 50 well-known repositioned drugs and (2) 12 drugs recently patented for new indications. In the first set, sensitivity values varied from 0.64 (TarPred) to 1.00 (PASS Online) for the initial indications and from 0.64 (TarPred) to 0.98 (PASS Online) for the repurposed indications. In the second set, sensitivity values varied from 0.08 (SuperPred) to 1.00 (PASS Online) for the initial indications and from 0.00 (SuperPred) to 1.00 (PASS Online) for the repurposed indications. Thus, this analysis demonstrated that the performance of machine learning methods surpassed those of chemical similarity assessments, particularly in the case of novel repurposed indications.

MetaTox: Web Application for Predicting Structure and Toxicity of Xenobiotics’ Metabolites

Abstract: A new freely available web-application MetaTox (http://www.way2drug.com/mg) for prediction of xenobiotic’s metabolism and calculation toxicity of metabolites based on the structural formula of chemicals has been developed. MetaTox predicts metabolites, which are formed by nine classes of reactions (aliphatic and aromatic hydroxylation, N- and O-glucuronidation, N-, S- and C-oxidation, and N- and O-dealkylation). The calculation of probability for generated metabolites is based on analyses of “structure-biotransformation reactions” and “structure-modified atoms” relationships using a Bayesian approach. Prediction of LD50 values is performed by GUSAR software for the parent compound and each of the generated metabolites using quantitative structure–activity relationahip (QSAR) models created for acute rat toxicity with the intravenous type of administration.

Computational platform Way2Drug: from the prediction of biological activity to drug repurposing

Abstract: The Way2Drug informational-computational platform (www.way2drug.com/dr) provides access to the data on drugs approved for medicinal use in the USA and Russian Federation, as well as computational possibilities for the prediction of biological activity of drug-like organic compounds. Currently realized computational tools of the platform, which allow one to predict several thousands of biological activity types, including the interaction with molecular targets, pharmacotherapeutic and side effects, metabolism, acute toxicity for rats, cytotoxicity, influence on gene expression, and other properties characterizing the evaluation how promising are particular drug-like compounds as potential pharmaceuticals, are reviewed. Using the Way2Drug platform, one can not only select the most promising "hits" for the synthesis and testing of biological activity but also reveal new indications for the launched drugs.

QSAR without borders

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.

Bad Pharma: How Drug Companies Mislead Doctors and Harm Patients. by Ben Goldacre

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National Institute of Allergy and Infectious Diseases, NIH

Abstract: National Institute of Allergy and Infectious Diseases, NIH , National Institute of Allergy and Infectious Diseases, NIH , کتابخانه دانشگاه علوم پزشکی و خدمات درمانی بوشهر