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The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of a community effort, the “Billion molecules against Covid-19 challenge”, to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 potentially active molecules, which were subsequently ranked to find ‘consensus compounds’. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (Nsp12 domain), and (alpha) spike protein S. Overall, 27 potential inhibitors were experimentally confirmed by binding-, cleavage-, and/or viral suppression assays and are presented here. All results are freely available and can be taken further downstream without IP restrictions. Overall, we show the effectiveness of computational techniques, community efforts, and communication across research fields (i.e., protein expression and crystallography, in silico modeling, synthesis and biological assays) to accelerate the early phases of drug discovery.

Motivation Application of chemical named entity recognition (CNER) algorithms allows retrieval of information from texts about chemical compound identifiers and creates associations with physical–chemical properties and biological activities. Scientific texts represent low-formalized sources of information. Most methods aimed at CNER are based on machine learning approaches, including conditional random fields and deep neural networks. In general, most machine learning approaches require either vector or sparse word representation of texts. Chemical named entities (CNEs) constitute only a small fraction of the whole text, and the datasets used for training are highly imbalanced. Methods and results We propose a new method for extracting CNEs from texts based on the naïve Bayes classifier combined with specially developed filters. In contrast to the earlier developed CNER methods, our approach uses the representation of the data as a set of fragments of text (FoTs) with the subsequent preparati`on of a set of multi-n-grams (sequences from one to n symbols) for each FoT. Our approach may provide the recognition of novel CNEs. For CHEMDNER corpus, the values of the sensitivity (recall) was 0.95, precision was 0.74, specificity was 0.88, and balanced accuracy was 0.92 based on five-fold cross validation. We applied the developed algorithm to the extracted CNEs of potential Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro) inhibitors. A set of CNEs corresponding to the chemical substances evaluated in the biochemical assays used for the discovery of Mpro inhibitors was retrieved. Manual analysis of the appropriate texts showed that CNEs of potential SARS-CoV-2 Mpro inhibitors were successfully identified by our method. Conclusion The obtained results show that the proposed method can be used for filtering out words that are not related to CNEs; therefore, it can be successfully applied to the extraction of CNEs for the purposes of cheminformatics and medicinal chemistry.

The growing amount of experimental data on chemical objects includes properties of small molecules, results of studies of their interaction with human and animal proteins, and methods of synthesis of organic compounds (OCs). The data obtained can be used to identify the names of OCs automatically, including all possible synonyms and relevant data on the molecular properties and biological activity. Utilization of different synonymic names of chemical compounds allows researchers to increase the completeness of data on their properties available from publications. Enrichment of the data on the names of chemical compounds by information about their possible metabolites can help estimate the biological effects of parent compounds and their metabolites more thoroughly. Therefore, an attempt at automated extraction of the names of parent compounds and their metabolites from the texts is a rather important task. In our study, we aimed at developing a method that provides the extraction of the named entities (NEs) of parent compounds and their metabolites from abstracts of scientific publications. Based on the application of the conditional random fields' algorithm, we extracted the NEs of chemical compounds. We developed a set of rules allowing identification of parent compound NEs and their metabolites in the texts. We evaluated the possibility of extracting the names of potential metabolites based on cosine similarity between strings representing names of parent compounds and all other chemical NEs found in the text. Additionally, we used conditional random fields to fetch the names of parent compounds and their metabolites from the texts based on the corpus of texts labeled manually. Our computational experiments showed that usage of rules in combination with cosine similarity could increase the accuracy of recognition of the names of metabolites compared to the rule-based algorithm and application of a machine-learning algorithm (conditional random fields).

Metabolic stability refers to the susceptibility of compounds to the biotransformation; it is characterized by such pharmacokinetic parameters as half-life (T1/2) and clearance (CL). Generally, these parameters are estimated by in vitro assays, which are based on cells or subcellular fractions (mainly liver microsomal enzymes) and serve as models of the processes occurring in living organisms. Data obtained from the experiments are used to build QSAR (Quantitative Structure-Activity Relationship) models. More than 8000 compounds with known CL and/or T1/2 values obtained in vitro using human liver microsomes were selected from the freely available ChEMBL v.27 database. GUSAR (General Unrestricted Structure-Activity Relationships) and PASS (Prediction of Activity Spectra for Substances) softwares were used to make quantitative and classification models. The quality of the models was evaluated using 5-fold cross-validation. Compounds were subdivided into "stable" and "unstable" by means of the following threshold parameters: T1/2 = 30 minutes, CL = 20 ml/min/kg. The accuracy of the models ranged from 0.5 (calculated in 5-fold CV on the test set for the half-life prediction quantitative model) to 0.96 (calculated in 5-fold CV on the test set for the clearance prediction classification model).Metabolicheskaia stabil'nost' opredeliaet stepen' ustoĭchivosti lekarstvenno-podobnykh soedineniĭ k biotransformatsii v organizme cheloveka i kharakterizuetsia takimi farmakokineticheskimi parametrami kak period poluvyvedeniia (T1/2) i klirens (CL). Kak pravilo, dlia ikh otsenki ispol'zuiutsia in vitro sistemy na osnove kletok ili subkletochnykh fraktsiĭ (v osnovnom mikrosomal'nye fermenty pecheni), kotorye sluzhat modeliami protsessov, protekaiushchikh v zhivom organizme. Poluchennye v rezul'tate éksperimentov dannye ispol'zuiutsia dlia postroeniia QSAR (Quantitative Structure-Activity Relationship) modeleĭ. Na osnove svobodno dostupnoĭ bazy dannykh ChEMBL v.27 bylo otobrano bolee 8000 zapiseĭ, soderzhashchikh struktury soedineniĭ i znacheniia ikh klirensa i/ili perioda poluvyvedeniia, poluchennye in vitro na mikrosomakh pecheni cheloveka. Dlia polucheniia kolichestvennykh i klassifikatsionnykh modeleĭ bylo ispol'zovano programmnoe obespechenie GUSAR (General Unrestricted Structure-Activity Relationships) i PASS (Prediction of Activity Spectra for Substances). Dlia otsenki kachestva modeleĭ ispol'zovalas' 5-kratnaia perekrestnaia proverka. Dlia razdeleniia soedineniĭ na stabil'nye i ne stabil'nye byli vybrany porogi T1/2=30 min, CL=20 ml/min/kg. Tochnost' modeleĭ var'iruetsia ot 0,5 (poschitano na testovoĭ vyborke dlia kolichestvennoĭ modeli prognoza vremeni poluvyvedeniia pri 5-kratnoĭ perekrestnoĭ proverke) do 0,96 (poschitano na testovoĭ vyborke dlia klassifikatsionnoĭ modeli prognoza klirensa pri 5-kratnoĭ perekrestnoĭ proverke).

The huge space of experimental data on biological and chemical objects and their interactions contributes to the rapid growth of scientific publications containing their analysis. Such data may include characteristics of low-molecular-weight compounds, results of their biological activity evaluation, and their interaction with human and animal proteins, methods of synthesis of organic compounds, and their classification. The past decades saw the development of methods for automated extraction of data from texts of scientific publications, including those for retrieval of names of organic compounds. These data can be used for the automatic identification of the names of organic compounds, including all possible synonyms. Since the topics of scientific publications are diverse, the extracted data can be applied to obtain information about (1) classification of organic compounds (2) methods of synthesis of a given organic compound; (3) physicochemical properties of this compound; (4) its interaction with high-molecular-weight compounds (including proteins, mRNA of animals and humans); and (5) the therapeutic properties of organic compounds, the active substance of the drug, and data on clinical trials. This review considers the methods aimed at searching and extracting data on names of low-molecular-weight compounds and interactions between them and animal and human proteins (biological objects), as well as data on experimentally confirmed biological activity and the effects of organic compounds (including drugs) on pathological processes. Here, we discuss the methods developed and the results of their application published over the past 10 years.

Sinonasal neuroblastoma is a rare malignant tumor with a wide variability of clinical manifestations, which may cause diagnostic difficulty and have hindered the progress in understanding the clinical course and improving outcomes of treatment. Patients often present with nasal obstruction, rhinorrhea, recurrent epistaxis, hyposmia, or anosmia. Treatment options consist of surgical resection and before or followed radiation and chemotherapy. We present a case report and short literature review about diagnostic, clinical and histological classification and treatment of this tumor.

Next Generation Sequencing (NGS) technologies are rapidly entering clinical practice. A promising area for their use lies in the field of newborn screening. The mass screening of newborns using NGS technology leads to the discovery of a large number of new missense variants that need to be assessed for association with the development of hereditary diseases. Currently, the primary analysis and identification of pathogenic variations is carried out using bioinformatic tools. Although extensive efforts have been made in the computational approach to variant interpretation, there is currently no generally accepted pathogenicity predictor. In this study, we used the sequence–structure–property relationships (SSPR) approach, based on the representation of protein fragments by molecular structural formula. The approach predicts the pathogenic effect of single amino acid substitutions in proteins related with twenty-five monogenic heritable diseases from the Uniform Screening Panel for Major Conditions recommended by the Advisory Committee on Hereditary Disorders in Newborns and Children. In order to create SSPR models of classification, we modified a piece of cheminformatics software, MultiPASS, that was originally developed for the prediction of activity spectra for drug-like substances. The created SSPR models were compared with traditional bioinformatic tools (SIFT 4G, Polyphen-2 HDIV, MutationAssessor, PROVEAN and FATHMM). The average AUC of our approach was 0.804 ± 0.040. Better quality scores were achieved for 15 from 25 proteins with a significantly higher accuracy for some proteins (IVD, HADHB, HBB). The best SSPR models of classification are freely available in the online resource SAV-Pred (Single Amino acid Variants Predictor).

In vitro cell-line cytotoxicity is widely used in the experimental studies of potential antineoplastic agents and evaluation of safety in drug discovery. In silico estimation of cytotoxicity against hundreds of tumor cell lines and dozens of normal cell lines considerably reduces the time and costs of drug development and the assessment of new pharmaceutical agent perspectives. In 2018, we developed the first freely available web application (CLC-Pred) for the qualitative prediction of cytotoxicity against 278 tumor and 27 normal cell lines based on structural formulas of 59,882 compounds. Here, we present a new version of this web application: CLC-Pred 2.0. It also employs the PASS (Prediction of Activity Spectra for Substance) approach based on substructural atom centric MNA descriptors and a Bayesian algorithm. CLC-Pred 2.0 provides three types of qualitative prediction: (1) cytotoxicity against 391 tumor and 47 normal human cell lines based on ChEMBL and PubChem data (128,545 structures) with a mean accuracy of prediction (AUC), calculated by the leave-one-out (LOO CV) and the 20-fold cross-validation (20F CV) procedures, of 0.925 and 0.923, respectively; (2) cytotoxicity against an NCI60 tumor cell-line panel based on the Developmental Therapeutics Program’s NCI60 data (22,726 structures) with different thresholds of IG50 data (100, 10 and 1 nM) and a mean accuracy of prediction from 0.870 to 0.945 (LOO CV) and from 0.869 to 0.942 (20F CV), respectively; (3) 2170 molecular mechanisms of actions based on ChEMBL and PubChem data (656,011 structures) with a mean accuracy of prediction 0.979 (LOO CV) and 0.978 (20F CV). Therefore, CLC-Pred 2.0 is a significant extension of the capabilities of the initial web application.

Drug repositioning – the discovery of new indications for the launched drugs – is the rapid natural response to viral infection SARS-CoV-2/COVID-19. We will consider the current state of research and development of repositioned pharmaceuticals for the therapy of COVID-19 using in silico and in vitro methods.

A new web-service predicting probability of T-cell receptor binding with major histocompatibility complex has been created based on the PASS software. Web-service is available on the link: http://way2drug.com/TCR-pred/ index.php

In the modern world of the dynamics of the world economy, the market for goods, works and services is full of offers in comparison with demand. And unlike the performance of work and services, a product can be regarded not only as a consumer product that meets the needs of people, but also as a means of enrichment by manufacturing companies in the conditions of creating increased demand for the product as a result of artificially reduced use period. This article reveals the range of problems faced by the consumer of goods when the manufacturer specifically programs aging and failure of equipment in order to increase its own enrichment. Based on the study of the judicial practice of foreign countries, where a number of large companies-manufacturers of electronic devices and home appliances have been held liable before the law for their actions aimed at reducing the actual service life of goods, it is important to pay attention to the term used by foreign countries regarding the service life of goods — «premature aging». As a result, the article contains proposals for improving the current legislation aimed at protecting consumer rights and preventing offenses in the same area.

The work is devoted to the study of pathologic-anatomical and microscopic changes in tubular (duodenum, jejunum, cecum) and parenchymal (liver, lungs, spleen, kidneys) organs of quails for eimeriosis caused by E. tenella, E. necatrix, E. acervulina. The work was carried out on the basis of the pathomorphological laboratory of the Department of anatomy and histology of Zhytomyr national agroecological University (Zhytomyr, Ukraine). Average invasion intensity after E. tenella, E. necatrix, E. acervulina was 4,3, 6,1 and 4,3 oocytes in 1 g of dung. The generally accepted classical methods of pathomorphological studies were used. It is established that the development of the pathological process in the body of quails with eimeriosis has a multi-organ character. The main pathologic-anatomical and microscopic changes were revealed in the cecum - serous catarrhal and hemorrhagic inflammation, necrosis of mucosal structures, the presence of Eimeria of different stages of development at the base of the villi and around the crypts of the mucosa, among the components of serous and hemorrhagic exudate in the lumen of the intestines. Lesions of the duodenum and jejunum were manifested by catarrhal mucous inflammation. Regarding parenchymal organs, granular and fatty dystrophy of hepatocytes, venous hyperemia, necrosis, infiltration of the parenchyma by lymphocytes were registered in the liver; in lungs-stagnant hyperemia and edema; in spleen-hypoplasia of lymphoid nodules, growth of connective tissue, infiltration of parenchyma by lymphoid cells; in kidneys-hyperemia and hemorrhages, granular dystrophy of epithelial cells of renal tubules, proliferative intracapillary glomerulonephritis. Pathomorphological changes in the studied organs of quails infested with E. tenella, E. necatrix, E. acervulina. Eimeria species are established, expand the available information on the pathogenesis of eimeriosis in birds and can be used as tests of differential diagnosis and in the development, organization of scientifically based therapeutic and preventive measures in quail farms of Ukraine.

Discovery of new antibacterial agents is a never-ending task of medicinal chemistry. Every new drug brings significant improvement to patients with bacterial infections, but prolonged usage of antibacterials leads to the emergence of resistant strains. Therefore, novel active structures with new modes of action are required. We describe a web application called AntiBac-Pred aimed to help users in the rational selection of the chemical compounds for experimental studies of antibacterial activity. This application is developed using antibacterial activity data available in ChEMBL and PASS software. It allows users to classify chemical structures of interest into growth inhibitors or noninhibitors of 353 different bacteria strains, including both resistant and nonresistant ones.

Metabolite identification is an essential part of the drug discovery and development process. Experimental methods allow identifying metabolites and estimating their relative amount, but they require cost-intensive and time-consuming techniques. Computational methods for metabolite prediction are devoid of these shortcomings and may be applied at the early stage of drug discovery. In this study, we investigated the possibility of creating SAR models for the prediction of the qualitative metabolite yield ('major', 'minor', "trace" and "negligible") depending on species and biological experimental systems. In addition, we have created models for prediction of xenobiotic excretion depending on its administration route for different species. The prediction is based on an algorithm of naïve Bayes classifier implemented in PASS software. The average accuracy of prediction was 0.91 for qualitative metabolite yield prediction and 0.89 for prediction of xenobiotic excretion. The created models were included as a component of MetaTox web application, which allows predicting the xenobiotic metabolism pathways ( http://www.way2drug.com/mg ).

Simultaneous use of the drugs may lead to undesirable Drug-Drug Interactions (DDIs) in the human body. Many DDIs are associated with changes in drug metabolism that performed by Drug-Metabolizing Enzymes (DMEs). In this case, DDI manifests itself as a result of the effect of one drug on the biotransformation of other drug(s), its slowing down (in the case of inhibiting DME) or acceleration (in case of induction of DME), which leads to a change in the pharmacological effect of the drugs combination. We used OpeRational ClassificAtion (ORCA) system for categorizing DDIs. ORCA divides DDIs into five classes: contraindicated (class 1), provisionally contraindicated (class 2), conditional (class 3), minimal risk (class 4), no interaction (class 5). We collected a training set consisting of several thousands of drug pairs. Algorithm of PASS program was used for the first, second and third classes DDI prediction. Chemical descriptors called PoSMNA (Pairs of Substances Multilevel Neighbourhoods of Atoms) were developed and implemented in PASS software to describe in a machine-readable format drug substances pairs instead of the single molecules. The average accuracy of DDI class prediction is about 0.84. A freely available web resource for DDI prediction was developed (http://way2drug.com/ddi/).

Most xenobiotics undergo metabolic conversions in the human body. The biological activity, toxicity, and other properties of such metabolites may significantly differ from those of the parent compounds. Not only xenobiotics and their final metabolites produced in large quantities, but the intermediates and final metabolites formed in trace amounts, can cause undesirable effects. We have developed a freely available web application MetaTox ( http://www.way2drug.com/mg/ ) for integral assessment of xenobiotics toxicity taking into account their metabolism in humans. The generation of the metabolite structures is based on the reaction fragments. The probability estimation of the certain reaction and the probability estimation of the atoms, which are changed during biotransformation, are used for generation of the xenobiotic metabolism pathways. The MetaTox web application assesses metabolism of compounds in humans and evaluates their acute toxicity, specific (cardiotoxicity, hepatotoxicity, nephrotoxicity), and chronic toxicity (carcinogenicity, teratogenicity, mutagenicity, effects on the reproductive system).

Human immunodeficiency virus (HIV) infection remains one of the most severe problems for humanity, particularly due to the development of HIV resistance. To evaluate an association between viral sequence data and drug combinations and to estimate an effect of a particular drug combination on the treatment results, collection of the most representative drug combinations used to cure HIV and the biological data on amino acid sequences of HIV proteins is essential. We have created a new, freely available web database containing 1,651 amino acid sequences of HIV structural proteins [reverse transcriptase (RT), protease (PR), integrase (IN), and envelope protein (ENV)], treatment history information, and CD4+ cell count and viral load data available by the user’s query. Additionally, the biological data on new HIV sequences and treatment data can be stored in the database by any user followed by an expert’s verification. The database is available on the web at http://www.way2drug.com/rhivdb.

The rapid spread of viral infections leads to a considerable amount of newly infected people with a high risk of severe complications and death. The ease of virus transmission provides the rapid spread of viruses among people and results in a high social significance of viral infections.

Most drug-like compounds can interact with several pharmacological targets and exhibit complex biological activity spectra. Analysis of these spectra helps find and optimize new pharmaceutical agents or identify new uses for approved and investigational drugs (drug repurposing). Since most pharmaceuticals usually undergo biotransformation in the human body, it is reasonable during drug discovery to take into account biological activity spectra of metabolites. A new freely available MetaPASS web application (http://www.way2drug.com/metapass) has been developed for analyzing the probable biological activity spectra of drug-like organic compounds taking into account their metabolites - integrated activity profile. To obtain an integrated biological activity profile, one can create a biotransformation network for any compound or analyze known networks for more than 950 compounds from ChEBML and DrugBank. Biological activity profile prediction is based on the PASS Refined software that predicts 1,333 biological activities with an average accuracy (IAP, calculated by leave-one-out cross-validation procedure) exceeded 0.97.

Постійний стан водно-електролітного балансу є обов'язковою умовою життєдіяльності організму. Порушення гомеостазу спричиняють значні зміни в усіх системах організму, в тому числі і ендокринній. Метою роботи є вивчення морфологічних та морфометричних перебудов судинного русла аденогіпофіза статевозрілих щурів-самців в умовах експериментальної позаклітинної дегідратації середнього ступеня. Експеримент проведено на 12 білих статевозрілих щурах-самцях масою 250-300г, віком 7-8 місяців. Застосовувалися морфометричні, статистичні та загальноприйняті методики мікроанатомічного (гістологічного) дослідження. Морфологічні перебудови судинного русла пов'язані з розвитком поліцитемічної гіповолемії та як наслідок, гіпоксії циркуляторного типу. Виявляються порушення кровонаповнення судин (дифузне венозно-капілярне повнокров'я), зміни реологічних властивостей крові (стаз, з початковими етапами сладжування та тромбоутворення) та перші ознаки збільшення проникності судинної стінки з формуванням поодиноких дрібновогнищевих діапедезних мікрогеморагій. Виразність морфологічних перебудов судинної стінки найбільша у субкапсулярній зоні. Перебудови судинного русла аденогіпофіза негативно впливають на морфофункціональну та секреторну активність ендокриноцитів, процеси евакуації гормонів у судинне русло, розвиток в організмі компенсаторно-пристосувальних процесів і перебіг загального адаптаційного синдрому у відповідь на дію пошкоджуючого агента., Background. The constant composition of the water-electrolyte balance is a necessary condition for the vital activity of the organism. Violations of homeostasis cause significant changes in all systems of the body, including endocrine. Objective. The purpose of the work is to study the morphological and morphometric alterations of the vascular bed of adenohypophysis in sexually mature male rats under conditions of experimental secondary-depletion extracellular dehydration. Methods. The experiment was conducted on 12 white, sexually mature male rats weighing 250-300 g aged 7-8 months. The morphometric, statistical and generally accepted methods of microanatomical (histological) research were used. Morphological changes in the vascular bed are associated with the development of polycythemic hypovolemia and, as a consequence, hypoxia of the circulatory type. Results. Changes in the blood flow to the blood vessels (diffuse venous-capillary plethora), changes in the rheological properties of the blood (stasis with the initial stages of slimming and thrombotic formation), and the first signs of increased permeability of the vascular wall with the formation of single small-basal diaphragmatic microhemorrhages are revealed. Conclusion. The most pronounced morphological alterations of the vascular wall in the subcapsular zone of the gland. Adjustment of the vascular bed of adenohypophysis negatively affects the morphofunctional and secretory activity of endocrinocytes, the processes of evacuation of hormones in the vascular bed, the development of compensatory and adaptive processes in the body and the course of the general adaptation syndrome in response to the action of the damaging agent., Постоянный состав водно-электролитного баланса является обязательным условием жизнедеятельности организма. Нарушения гомеостаза вызывают значительные изменения во всех системах организма, в том числе и эндокринной. Целью работы является изучение морфологических и морфометрических перестроек сосудистого русла аденогипофиза половозрелых крыс-самцов в условиях экспериментальной внеклеточной дегидратации средней степени. Эксперимент проведен на 12 белых половозрелых крысах-самцах массой 250-300г в возрасте 7-8 месяцев. Применялись морфометрические, статистические и общепринятые методики микроанатомичного (гистологического) исследования. Морфологические перестройки сосудистого русла связаны с развитием полицитемической гиповолемии и как следствие, гипоксии циркуляторного типа. Выявляются нарушения кровенаполнения сосудов (диффузное венозно-капиллярное полнокровие), изменения реологических свойств крови (стаз с начальными этапами сладжирования и тромбообразования) и первые признаки увеличения проницаемости сосудистой стенки с формированием одиночных мелкоочаговых диапедезных микрогеморагий. Наиболее выражены морфологические перестройки сосудистой стенки в субкапсулярной зоне железы. Перестройки сосудистого русла аденогипофиза негативно влияют на морфофункциональную и секреторную активность эндокриноцитов, процессы эвакуации гормонов в сосудистое русло, развитие в организме компенсаторно-приспособительных процессов и ход общего адаптационного синдрома в ответ на действие повреждающего агента.

Molecular Property Diagnostic Suite - Diabetes Mellitus (MPDSDM) is a Galaxy-based, open source disease-specific web portal for diabetes. It consists of three modules namely (i) data library (ii) data processing and (iii) data analysis tools. The data library (target library and literature) module provide extensive and curated information about the genes involved in type 1 and type 2 diabetes onset and progression stage (available at http://www.mpds-diabetes.in). The database also contains information on drug targets, biomarkers, therapeutics and associated genes specific to type 1, and type 2 diabetes. A unique MPDS identification number has been assigned for each gene involved in diabetes mellitus and the corresponding card contains chromosomal data, gene information, protein UniProt ID, functional domains, druggability and related pathway information. One of the objectives of the web portal is to have an open source data repository that contains all information on diabetes and use this information for developing therapeutics to cure diabetes. We also make an attempt for computational drug repurposing for the validated diabetes targets. We performed virtual screening of 1455 FDA approved drugs on selected 20 type 1 and type 2 diabetes proteins using docking protocol and their biological activity was predicted using "PASS Online" server (http://www.way2drug.com/passonline) towards anti-diabetic activity, resulted in the identification of 41 drug molecules. Five drug molecules (which are earlier known for anti-malarial/microbial, anti-viral, anti-cancer, anti-pulmonary activities) were proposed to have a better repurposing potential for type 2 anti-diabetic activity and good binding affinity towards type 2 diabetes target proteins.

Background: Phenazepam (bromdihydrochlorphenylbenzodiazepine) is the original Russian benzodiazepine tranquilizer belonging to 1,4-benzodiazepines. There is still limited knowledge about phenazepam’s metabolic liver pathways and other pharmacokinetic features. Methods: To determine phenazepam’s metabolic pathways, the study was divided into three stages: in silico modeling, in vitro experiment (cell culture study), and in vivo confirmation. In silico modeling was performed on the specialized software PASS and GUSAR to evaluate phenazepam molecule affinity to different cytochromes. The in vitro study was performed using a hepatocytes’ cell culture, cultivated in a microbioreactor to produce cytochrome P450 isoenzymes. The culture medium contained specific cytochrome P450 isoforms inhibitors and substrates (for CYP2C9, CYP3A4, CYP2C19, and CYP2B6) to determine the cytochrome that was responsible for phenazepam’s metabolism. We also measured CYP3A activity using the 6-betahydroxycortisol/cortisol ratio in patients. Results: According to in silico and in vitro analysis results, the most probable metabolizer of phenazepam is CYP3A4. By the in vivo study results, CYP3A activity decreased sufficiently (from 3.8 [95% CI: 2.94–4.65] to 2.79 [95% CI: 2.02–3.55], p=0.017) between the start and finish of treatment in patients who were prescribed just phenazepam. Conclusions: Experimental in silico and in vivo studies confirmed that the original Russian benzodiazepine phenazepam was the substrate of CYP3A4 isoenzyme.

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.

Toxicity and severe adverse effects are the primary cause of drug-candidate failures at the late stages of preclinical and clinical trials. Since most xenobiotics undergo biotransformations, their interaction with human organism reveals the effects produced by parent compounds and all metabolites. To increase the chances of successful drug development, estimation of the entire toxicity for drug substance and its metabolites is necessary for filtering out the potentially toxic compounds. We proposed the computational approach to the integral evaluation of xenobiotics’ toxicity based on the structural formula of the drug-like compound. In the framework of this study, the consensus QSAR model was developed based on the analysis of over 3000 compounds with information about their rat acute toxicity for intravenous route of administration. Four different numerical methods, estimating the integral toxicity, were proposed, and their comparative performance was studied using the external evaluation set consisting of 37 structures of drugs and 200 their metabolites. It was shown that, on the average, the best correspondence between the predicted and published data is obtained using the method that takes into account the estimated characteristics for both the parent compound and its most toxic metabolite.

Motivation Identification of new molecules promising for treatment of HIV-infection and HIV-associated disorders remains an important task in order to provide safer and more effective therapies. Utilization of prior knowledge by application of computer-aided drug discovery approaches reduces time and financial expenses and increases the chances of positive results in anti-HIV R&D. To provide the scientific community with a tool that allows estimating of potential agents for treatment of HIV-infection and its comorbidities, we have created a freely-available web-resource for prediction of relevant biological activities based on the structural formulae of drug-like molecules. Results Over 50 000 experimental records for anti-retroviral agents from ChEMBL database were extracted for creating the training sets. After careful examination, about seven thousand molecules inhibiting five HIV-1 proteins were used to develop regression and classification models with the GUSAR software. The average values of R2 = 0.95 and Q2 = 0.72 in validation procedure demonstrated the reasonable accuracy and predictivity of the obtained (Q)SAR models. Prediction of 81 biological activities associated with the treatment of HIV-associated comorbidities with 92% mean accuracy was realized using the PASS program. Availability and implementation Freely available on the web at http://www.way2drug.com/hiv/. Supplementary information Supplementary data are available at Bioinformatics online.

The majority of xenobiotics undergo a number of chemical reactions known as biotransformation in human body. The biological activity, toxicity, and other properties of the metabolites may significantly differ from those of the parent compound. Not only xenobiotic itself and its final metabolites produced in large quantities, but the intermediate and final metabolites that are formed in trace quantities, can cause undesirable effects. We have developed a freely available web resource MetaTox (http://www.way2drug.com/mg/) for integral assessment of xenobiotics toxicity taking into account their metabolism in the humans. The generation of the metabolite structures is based on the reaction fragments. The estimates of the probability of the reaction of a certain class and the probability of site of biotransformation are used at the generation of the xenobiotic metabolism pathways. The web resource MetaTox allows researchers to assess the metabolism of compounds in the humans and to obtain assessment of their acute, chronic toxicity, and adverse effects.Bol'shinstvo ksenobiotikov, popadaia v organizm cheloveka, podvergaiutsia metabolizmu. Biologicheskaia aktivnost', toksichnost' i drugie svoĭstva metabolitov mogut znachitel'no otlichat'sia ot takovykh u iskhodnykh veshchestv. Ne tol'ko ksenobiotiki i ikh konechnye metabolity, obrazuiushchiesia v bol'shikh kolichestvakh, no i promezhutochnye i konechnye metabolity, obrazuiushchiesia v sledovykh kolichestvakh, mogut vyzyvat' nezhelatel'nye posledstviia. My razrabotali svobodno dostupnyĭ veb-resurs MetaTox (http://www.way2drug.com/mg/) dlia integral'noĭ otsenki toksichnosti ksenobiotikov s uchetom ikh metabolizma v organizme cheloveka. Generatsiia struktur metabolitov vypolniaetsia na osnove fragmentov strukturnykh izmeneniĭ. Pri postroenii puti metabolizma ispol'zuiutsia otsenki veroiatnosteĭ reaktsiĭ opredelennogo klassa i saĭtov metabolizma. Veb-resurs MetaTox pozvoliaet otsenit' puti metabolizma soedineniĭ v organizme cheloveka i poluchit' otsenku ikh ostroĭ, spetsificheskoĭ (kardiotoksichnost', gepatotoksichnost', nefrotoksichnost') i khronicheskoĭ toksichnosti (kantserogennost', teratogennost', mutagennost', vliianie na reproduktivnuiu sistemu).

Most pharmaceutical substances interact with several or even many molecular targets in the organism, determining the complex profiles of their biological activity. Moreover, due to biotransformation in the human body, they form one or several metabolites with different biological activity profiles. Therefore, the development and rational use of novel drugs requires the analysis of their biological activity profiles, taking into account metabolism in the human body. In silico methods are currently widely used for estimating new drug-like compounds&rsquo, interactions with pharmacological targets and predicting their metabolic transformations. In this study, we consider the estimation of the biological activity profiles of organic compounds, taking into account the action of both the parent molecule and its metabolites in the human body. We used an external dataset that consists of 864 parent compounds with known metabolites. It is shown that the complex assessment of active pharmaceutical ingredients&rsquo, interactions with the human organism increases the quality of computer-aided estimates. The toxic and adverse effects showed the most significant difference: reaching 0.16 for recall and 0.14 for precision.

Drug-drug interaction (DDI) is the phenomenon of alteration of the pharmacological activity of a drug(s) when another drug(s) is co-administered in cases of so-called polypharmacy. There are three types of DDIs: pharmacokinetic (PK), pharmacodynamic, and pharmaceutical. PK is the most frequent type of DDI, which often appears as a result of the inhibition or induction of drug-metabolising enzymes (DME). In this review, we summarise in silico methods that may be applied for the prediction of the inhibition or induction of DMEs and describe appropriate computational methods for DDI prediction, showing the current situation and perspectives of these approaches in medicinal and pharmaceutical chemistry. We review sources of information on DDI, which can be used in pharmaceutical investigations and medicinal practice and/or for the creation of computational models. The problem of the inaccuracy and redundancy of these data are discussed. We provide information on the state-of-the-art physiologically- based pharmacokinetic modelling (PBPK) approaches and DME-based in silico methods. In the section on ligand-based methods, we describe pharmacophore models, molecular field analysis, quantitative structure-activity relationships (QSAR), and similarity analysis applied to the prediction of DDI related to the inhibition or induction of DME. In conclusion, we discuss the problems of DDI severity assessment, mention factors that influence severity, and highlight the issues, perspectives and practical using of in silico methods.

Online resources (PASS Online, SuperPred, SwissTargetPrediction and DRAR-CPI) for the prediction of biological activity of organic compounds from their structural formulas were considered. Based on a test set of drugs approved by 2014, the accuracies of predictions were compared. The four web resources can be arranged with respect to the quality of prediction (sensitivity, S) as follows: SwissTargetPrediction (S = 0.37) < DRAR-CPI (S = 0.41) < Super-Pred (S = 0.53) < PASS Online (S = 0.95). A conclusion was made that PASS Online employs superior machine learning algorithms based on MNA descriptors and Bayessian classifier in contrast to the similarity-based methods used in SuperPred and SwissTargetPrediction or the molecular docking methods used in DRAR-CPI. Possible reasons for the low prediction quality of SuperPred, SwissTargetPrediction, and DRAR-CPI are discussed and the development perspectives of this area of computational chemistry are given.

Applicability of our computer programs PASS and PharmaExpert for prediction of biological activity spectra of rather complex and structurally diverse phytocomponents of medicinal plants, both separately and in combinations has been evaluated. For this purpose we have created the web-resource containing known information about structural formulas and biological activity of 1906 phytocomponents of 50 Ayurvedic medicinal plants used in Traditional Indian Medicine (TIM) (http://ayurveda.pharmaexpert.ru). The PASS training set was updated by addition of information about structure and biological activity of 946 natural compounds; then the training procedure and validation were performed, to estimate the quality of PASS prediction. It was shown that the differences between the average accuracy of prediction obtained in leave-5%-out cross-validation (94.467%) and in leave-one-out cross-validation (94.605%) are very small thus demonstrating high predictive ability of the program. Results of biological activity spectra prediction for all phytocomponents included in our database coincided in 83.5% of cases with known experimental data. Additional types of biological activity predicted with high probability indicate further promising directions for further studies of certain phytocomponents of some medicinal plants. The analysis of prediction results of sets of phytocomponents in each of 50 medicinal plants was made by the PharmaExpert software. Based on this analysis, we found that the combination of phytocomponents from Passiflora incarnata may exhibit nootropic, anticonvulsant, and antidepressant effects. Experiments carried out in mice models confirmed the predicted effects of P. incarnata extracts.

Applicability of our computer programs PASS and PharmaExpert to prediction of biological activity spectra of rather complex and structurally diverse phytocomponents of medicinal plants, both separately and in combinations has been evaluated. The web-resource on phytochemicals of 50 medicinal plants used in Ayurveda was created for the study of hidden therapeutic potential of Traditional Indian Medicine (TIM) (http://ayurveda.pharmaexpert.ru). It contains information on 50 medicinal plants, their using in TIM and their pharmacology activities, also as 1906 phytocomponents. PASS training set was updated by addition of information about 946 natural compounds; then the training procedure and validation were performed, to estimate the quality of PASS prediction. It was shown that the difference between the average accuracy of prediction obtained in leave-5%-out cross-validation (94,467%) and in leave-one-out cross-validation (94,605%) is very small. These results showed high predictive ability of the program. Results of biological activity spectra prediction for all phytocomponents included in our database are in good correspondence with the experimental data. Additional kinds of biological activity predicted with high probability provide the information about most promising directions of further studies. The analysis of prediction results of sets of phytocomponents in each of 50 medicinal plants was made by PharmaExpert software. Based on this analysis, we found that the combination of phytocomponents from Passiflora incarnata may exhibit nootropic, anticonvulsant and antidepressant effects. Experiments carried out in mice models confirmed the predicted effects of Passiflora incarnata extracts.Issledovana primenimost' razrabotannykh nami komp'iuternykh programm PASS i PharmaExpert k analizu spektrov biologicheskoĭ aktivnosti dostatochno slozhnykh i raznoobraznykh po strukture fitokomponentov lekarstvennykh rasteniĭ, kak po-otdel'nosti, tak i v kombinatsiiakh. S étoĭ tsel'iu byla sozdana baza dannykh, soderzhashchaia izvestnuiu informatsiiu o strukturnykh formulakh i biologicheskoĭ aktivnosti 1906 fitokomponentov 50 lekarstvennykh rasteniĭ iz traditsionnoĭ indiĭskoĭ meditsiny (TIM) Aiurveda (ayurveda.pharmaexpert.ru). Obuchaiushchaia vyborka programmy PASS byla popolnena informatsieĭ o strukture i biologicheskoĭ aktivnosti 946 prirodnykh soedineniĭ; provedeny obuchenie i validatsiia, pozvolivshie otsenit' kachestvo prognoza PASS. Pokazano, chto razlichiia mezhdu znacheniiami sredneĭ tochnosti prognoza pri kross-validatsii s razbieniem vyborki na 20 chasteĭ (94,467%) i pri skol'ziashchem kontrole s iskliucheniem po odnomu (94,605%) neznachitel'ny, chto svidetel'stvuet o khorosheĭ prognosticheskoĭ sposobnosti programmy. Rezul'taty prognoza spektrov biologicheskoĭ aktivnosti dlia vsekh vkliuchennykh v nashu bazu dannykh fitokomponentov v 83,5% sluchaev sovpali s izvestnymi éksperimental'nymi dannymi. Predskazannye s vysokoĭ veroiatnost'iu dopolnitel'nye vidy aktivnosti ukazyvaiut na perspektivnye napravleniia dal'neĭshikh issledovaniĭ otdel'nykh fitokomponentov lekarstvennykh rasteniĭ. S pomoshch'iu komp'iuternoĭ programmy PharmaExpert my vypolnili analiz rezul'tatov prognoza dlia kombinatsiĭ fitokomponentov, soderzhashchikhsia v otdel'nykh lekarstvennykh rasteniiakh TIM. Na osnove étogo analiza ustanovleno, chto kombinatsiia fitokomponentov rasteniia Passiflora incarnata mozhet obladat' nootropnym, protivosudorozhnym i antidepressantnym éffektami. Poluchennye v éksperimentakh na myshinykh modeliakh rezul'taty podtverdili nalichie prognoziruemykh vidov biologicheskoĭ aktivnosti u ékstraktov Passiflora incarnata.

Application of structure-activity relationships (SARs) for the prediction of adverse effects of drugs (ADEs) has been reported in many published studies. Training sets for the creation of SAR models are usually based on drug label information which allows for the generation of data sets for many hundreds of drugs. Since many ADEs may not be related to drug consumption, one of the main problems in such studies is the quality of data on drug-ADE pairs obtained from labels. The information on ADEs may be included in three sections of the drug labels: "Boxed warning," "Warnings and Precautions," and "Adverse reactions." The first two sections, especially Boxed warning, usually contain the most frequent and severe ADEs that have either known or probable relationships to drug consumption. Using this information, we have created manually curated data sets for the five most frequent and severe ADEs: myocardial infarction, arrhythmia, cardiac failure, severe hepatotoxicity, and nephrotoxicity, with more than 850 drugs on average for each effect. The corresponding SARs were built with PASS (Prediction of Activity Spectra for Substances) software and had balanced accuracy values of 0.74, 0.7, 0.77, 0.67, and 0.75, respectively. They were implemented in a freely available ADVERPred web service ( http://www.way2drug.com/adverpred/ ), which enables a user to predict five ADEs based on the structural formula of compound. This web service can be applied for estimation of the corresponding ADEs for hits and lead compounds at the early stages of drug discovery.

Biotransformation is a process of the chemical modifications which may lead to the reactive metabolites, in particular the epoxides. Epoxide reactive metabolites may cause the toxic effects. The prediction of such metabolites is important for drug development and ecotoxicology studies. Epoxides are formed by some oxidation reactions, usually catalysed by cytochromes P450, and represent a large class of three-membered cyclic ethers. Identification of molecules, which may be epoxidized, and indication of the specific location of epoxide functional group (which is called SOE - site of epoxidation) are important for prediction of epoxide metabolites. Datasets from 355 molecules and 615 reactions were created for training and validation. The prediction of SOE is based on a combination of LMNA (Labelled Multilevel Neighbourhood of Atom) descriptors and Bayesian-like algorithm implemented in PASS software and MetaTox web-service. The average invariant accuracy of prediction (AUC) calculated in leave-one-out and 20-fold cross-validation procedures is 0.9. Prediction of epoxide formation based on the created SAR model is included as the component of MetaTox web-service ( http://www.way2drug.com/mg ).

Motivation Identification of rodent carcinogens is an important task in risk assessment of chemicals. SAR methods were proposed to reduce the number of animal experiments. Most of these methods ignore information about organ-specificity of tumorigenesis. Our study was aimed at the creation of classification models and a freely available online service for prediction of rodent carcinogens considering the species (rats, mice), sex and tissue-specificity from structural formula of compounds. Results The data from Carcinogenic Potency Database for 1011 organic compounds evaluated on the standard two-year rodent carcinogenicity bioassay was used for the creation of training sets. Structure-activity relationships models for prediction of rodent organ-specific carcinogenicity were created by PASS software, which was based on Bayesian-like approach and Multilevel Neighborhoods of Atoms descriptors. The average prediction accuracy for training sets calculated by leave-one-out and 10-fold cross-validation was 79 and 78.2%, respectively. Availability and implementation Freely available on the web at http://www.way2drug.com/ROSC. Supplementary information Supplementary data are available at Bioinformatics online.

The freely accessible web resource PASS Online is presented. This resource is designed for the prediction of the biological activity spectra of organic compounds based on their structural formulas for more than 4000 types of biological activity with average accuracy above 95% ( http://www.way2drug.com/passonline ). The prediction is based on an analysis of the structure-activity relationships in the training set containing information on the structure and biological activity of more than 300000 organic compounds. The possibilities and limitations of this approach are described. Recommendations are given for interpreting the prediction results. Examples are given for the practical use of the PASS Online web resource in order to establish priorities for chemical synthesis and biological testing of substances on the basis of prediction results. The further trends are considered for the using PASS Online as an Internet platform for joint projects of academic researchers for the search and development of new pharmaceutical agents.

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.

Summary: 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. Contact: rudik_anastassia@mail.ru Supplementary information: Supplementary data are available at Bioinformatics online.

Xenobiotics biotransformation in humans is a process of the chemical modifications, which may lead to the formation of toxic metabolites. The prediction of such metabolites is very important for drug development and ecotoxicology studies. We created the web-application MetaTox ( http://way2drug.com/mg ) for the generation of xenobiotics metabolic pathways in the human organism. For each generated metabolite, the estimations of the acute toxicity (based on GUSAR software prediction), organ-specific carcinogenicity and adverse effects (based on PASS software prediction) are performed. Generation of metabolites by MetaTox is based on the fragments datasets, which describe transformations of substrates structures to a metabolites structure. We added three new classes of biotransformation reactions: Dehydrogenation, Glutathionation, and Hydrolysis, and now metabolite generation for 15 most frequent classes of xenobiotic’s biotransformation reactions are available. MetaTox calculates the probability of formation of generated metabolite — it is the integrated assessment of the biotransformation reactions probabilities and their sites using the algorithm of PASS ( http://way2drug.com/passonline ). The prediction accuracy estimated by the leave-one-out cross-validation (LOO-CV) procedure calculated separately for the probabilities of biotransformation reactions and their sites is about 0.9 on the average for all reactions.

The phagocytic activity and production of proinflammatory cytokines by murine peritoneal macrophages was examined upon in vitro and in vivo exposure to low doses (up to 150 mJ/cm2) of red (650 nm) and infrared (850 nm) light generated by semiconductor lasers. Infrared irradiation proved stimulatory in a dose-dependent way for phagocytosis and killing of enteropathogenic Escherichia coli as well as for cytokine production. Red light markedly enhanced cytokine production but was detrimental to macrophage activity and at higher doses caused their destruction.

An essential characteristic of chemical compounds is their biological activity since its presence can become the basis for the use of the substance for therapeutic purposes, or, on the contrary, limit the possibilities of its practical application due to the manifestation of side action and toxic effects. Computer assessment of the biological activity spectra makes it possible to determine the most promising directions for the study of the pharmacological action of particular substances, and to filter out potentially dangerous molecules at the early stages of research. For more than 25 years, we have been developing and improving the computer program PASS (Prediction of Activity Spectra for Substances), designed to predict the biological activity spectrum of substance based on the structural formula of its molecules. The prediction is carried out by the analysis of structure-activity relationships for the training set, which currently contains information on structures and known biological activities for more than one million molecules. The structure of the organic compound is represented in PASS using Multilevel Neighborhoods of Atoms descriptors; the activity prediction for new compounds is performed by the naive Bayes classifier and the structure-activity relationships determined by the analysis of the training set. We have created and improved both local versions of the PASS program and freely available web resources based on PASS (http://www.way2drug.com). They predict several thousand biological activities (pharmacological effects, molecular mechanisms of action, specific toxicity and adverse effects, interaction with the unwanted targets, metabolism and action on molecular transport), cytotoxicity for tumor and non-tumor cell lines, carcinogenicity, induced changes of gene expression profiles, metabolic sites of the major enzymes of the first and second phases of xenobiotics biotransformation, and belonging to substrates and/or metabolites of metabolic enzymes. The web resource Way2Drug is used by over 19 000 researchers from more than 100 countries around the world, which allowed them to obtain over 600 000 predictions and publish about 500 papers describing the obtained results. The analysis of the published works shows that in some cases the interpretation of the prediction results presented by the authors of these publications requires an adjustment. In this work, we provide the theoretical basis and consider, on particular examples, the opportunities and limitations of computer-aided prediction of biological activity spectra., Важной характеристикой химических соединений является их биологическая активность, поскольку ее наличие может стать основой для использования вещества в терапевтических целях, либо, напротив, ограничить возможности его практического применения вследствие проявления побочных и токсических эффектов. Компьютерная оценка спектра биологической активности дает возможность определить наиболее перспективные направления для тестирования фармакологического действия конкретных веществ и отсеять потенциально опасные молекулы на ранних стадиях исследований. Свыше 25 лет нами осуществляется разработка и совершенствование компьютерной программы PASS (Prediction of Activity Spectra for Substances), предназначенной для прогнозирования спектра биологической активности вещества по структурной формуле его молекул. Прогноз осуществляется на основе анализа зависимостей «структура-активность» для соединений обучающей выборки, в настоящее время содержащей информацию о структурах и известных видах биологической активности более чем для миллиона молекул. Описание структуры молекул органического соединения реализовано в PASS посредством дескрипторов атомных окрестностей (Multilevel Neighborhoods of Atoms), прогнозирование активности для новых соединений выполняется алгоритмом на основе «наивного Байесовского подхода» и зависимостей «структура-активность», выявляемых при анализе обучающей выборки. Нами созданы и совершенствуются как локальные версии программы PASS, так и свободно доступные в Интернет веб-ресурсы на основе PASS (http://way2drug.com): прогноз нескольких тысяч видов биологической активности (фармакологические эффекты, молекулярные механизмы действия, специфическая токсичность и побочное действие, метаболизм, а также влияние на нежелательные мишени, молекулярный транспорт, генную экспрессию), прогноз цитотоксичности для опухолевых и неопухолевых клеточных линий, прогноз канцерогенности, прогноз индуцированных органическими соединениями изменений профилей экспрессии генов, прогноз взаимодействия с ферментами метаболизма лекарств, в том числе прогноз сайтов метаболизма, а также прогноз принадлежности к субстратам и/или метаболитам этих ферментов. Веб-ресурс Way2Drug используют свыше 19 тысяч исследователей более чем из 100 стран мира, что позволило им осуществить свыше 600 тысяч прогнозов и опубликовать около 500 работ с описанием полученных результатов. Анализ опубликованных работ показывает, что в некоторых случаях приводимая авторами этих публикаций интерпретация результатов прогноза требует корректировки. В рамках настоящей работы мы представим теоретическое обоснование и рассмотрим на конкретных примерах возможности и ограничения компьютерного прогнозирования спектров биологической активности.

Metabolism of xenobiotics (Greek xenos: exogenous substances) plays an essential role in the prediction of biological activity and testing for the subsequent research and development of new drug candidates. Integration of various methods and techniques using different computational and experimental approaches is one of the keys to a successful metabolism prediction. While multiple structure-based and ligand-based approaches to metabolism prediction exist, the most important problem arises at the first stage of metabolism prediction: detection of the sites of metabolism (SOMs). In this paper, we describe the application of Quantitative Neighborhoods of Atoms (QNA) descriptors for prediction of the SOMs using potential function method, as well as several different machine learning techniques: naïve Bayes, random forest classifier, multilayer perceptron with back propagation and convolutional neural networks, and deep neural networks.

Covering: up to 2014 In silico approaches have been widely recognised to be useful for drug discovery. Here, we consider the significance of available databases of medicinal plants and chemo- and bioinformatics tools for in silico drug discovery beyond the traditional use of folk medicines. This review contains a practical example of the application of combined chemo- and bioinformatics methods to study pleiotropic therapeutic effects (known and novel) of 50 medicinal plants from Traditional Indian Medicine.

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.

Summary: Experimentally found gene expression profiles are used to solve different problems in pharmaceutical studies, such as drug repositioning, resistance, toxicity and drug–drug interactions. A special web service, DIGEP-Pred, for prediction of drug-induced changes of gene expression profiles based on structural formulae of chemicals has been developed. Structure–activity relationships for prediction of drug-induced gene expression profiles were determined by Prediction of Activity Spectra for Substances (PASS) software. Comparative Toxicogenomics Database with data on the known drug-induced gene expression profiles of chemicals was used to create mRNA- and protein-based training sets. An average prediction accuracy for the training sets (ROC AUC) calculated by leave-one-out cross-validation on the basis of mRNA data (1385 compounds, 952 genes, 500 up- and 475 down-regulations) and protein data (1451 compounds, 139 genes, 93 up- and 55 down-regulations) exceeded 0.85. Availability: Freely available on the web at http://www.way2drug.com/GE. Contact: alexey.lagunin@ibmc.msk.ru Supplementary information: Supplementary data are available at Bioinformatics online.

The effect of low-intensity laser radiation generated by semiconductor devices in the red (650 nm) and infrared (850 nm) regions of the spectrum in vitro and in vivo on the phagocytic activity and synthesis of proinflammatory cytokines by peritoneal macrophages during the phagocytosis of bacterial cells has been studied. A culture of the clinical strain of the enteropathogenic bacterium Escherichia coli was used as an object. The radiation dose was varied by changing the power and duration of exposure. The results obtained indicate that infrared low-intensity laser radiation has a stimulating effect on the phagocytic activity of macrophages. It was shown that the effect of low-intensity laser radiation on the activity of the phagocytic process, the enhancement of the adhesion of bacteria by macrophages, killing of bacteria, and the production of proinflammatory cytokines is dose-dependent. The exposure to the rays of the red region of the spectrum on phagocytizing macrophages induced a decrease in their activity; as the dose was increased, the destruction of cells was registered.