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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.

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.

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.

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.

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.

Проблема тромбоемболії легеневої артерії (ТЕЛА) зберігає актуальність, оскільки залишається складною клінічною проблемою, незважаючи на сучасні методи діагностики та лікування. За даними ВООЗ, ТЕЛА є одним із найбільш поширених серцево-судинних захворювань у світі й посідає третє місце після ішемічної хвороби серця та інсульту. Емболія легеневої артерії не завжди є наслідком емболії тромботичними масами. Є і нетромботична легенева емболія (НТЕЛА), що визначається як емболізація легеневих артерій різними типами клітин (адипоцитами, амніотичними, трофобластичними або пухлинними), септичними емболами, грибами, сторонніми предметами або повітрям/газами.Мета роботи – підвищити рівень обізнаності лікарів щодо питання про нетромботичну емболію легеневої артерії сторонніми тілами.Описали клінічний випадок емболії легеневої артерії чужорідними тілами – спіралями, встановленими в лівій сіменній вені, для корекції варикоцеле. Незважаючи на чималу площу ураження, не виявили ознаки легеневої гіпертензії. Отже, можливості сучасної інтервенційної медицини, що стрімко розвиваються, потребують від лікаря обізнаності та настороженості щодо розвитку НТЕЛА. Комп’ютерна томографія з внутрішньовенним підсиленням є основним методом діагностики як ТЕЛА, так і нетромботbчної емболії легеневої артерії., The problem of pulmonary thromboembolism (PTE) continues to be highly relevant, as it remains a complex clinical problem today, in spite of modern diagnostic and treatment methods. According to WHO, PTE is recognized as one of the most common cardiovascular diseases in the world and ranks third after coronary heart disease and brain stroke. Embolism to the pulmonary artery is not always caused by thrombotic masses. There is also a non-thrombotic pulmonary embolism (NTPE), which is defined as embolization to the pulmonary arteries by different types of cells (adipocytes, amniotic, trophoblastic or tumor), fungi, septic and foreign material or air / gases. In our article, the clinical case of embolism to the pulmonary artery by foreign bodies – coils placed in the left spermatic vein for varicocele correction is presented. Despite the significant lesion area, signs of pulmonary hypertension were not revealed. Thus, the rapidly evolving potential of modern interventional medicine requires a doctor to be aware and alert as for a NTPE development. Computed tomographic angiopulmonography with the aid of intravenous contrast enhancement is the main diagnostic method for pulmonary thromboembolism as well as for non-thrombotic pulmonary embolism., Проблема тромбоэмболии легочной артерии (ТЭЛА) сохраняет высокую степень актуальности, так как остается сложной клинической проблемой, несмотря на современные методы диагностики и лечения. По данным ВОЗ, ТЭЛА признана одним из наиболее распространенных сердечно-сосудистых заболеваний в мире и занимает третье место после ишемической болезни сердца и инсульта. Эмболия легочной артерии не всегда является следствием эмболии тромботическими массами. Существует и нетромботическая легочная эмболия (НТЭЛА), определяющаяся как эмболизация легочных артерий различными типами клеток (адипоцитами, амниотическими, трофобластическими или опухолевыми), септическими эмболами, грибами, инородными телами или воздухом/газами.Цель работы – повысить осведомленность врачей о проблеме нетромботической эмболии легочной артерии.Приведен клинический случай эмболии легочной артерии инородными телами – спиралями, установленными в левой семенной вене, для коррекции варикоцеле. Несмотря на значительную площадь поражения, признаков легочной гипертензии обнаружено не было.Выводы. Стремительно развивающиеся возможности современной интервенционной медицины требуют от врача осведомленности и настороженности в отношении развития НТЭЛА. Компьютерная томография с внутривенным усилением является основным методом диагностики как ТЭЛА, так и нетромботической эмболии легочной артерии.

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.

Drug-drug interactions (DDIs) severity assessment is a crucial problem because polypharmacy is increasingly common in modern medical practice. Many DDIs are caused by alterations of the plasma concentrations of one drug due to another drug inhibiting and/or inducing the metabolism or transporter‐mediated disposition of the victim drug. Accurate assessment of clinically relevant DDIs for novel drug candidates represents one of the significant tasks of contemporary drug research and development and is important for practicing physicians. This work is a development of our previous investigations and aimed to create a model for the severity of DDIs prediction. PASS program and PoSMNA descriptors were implemented for prediction of all five classes of DDIs severity according to OpeRational ClassificAtion (ORCA) system: contraindicated (class 1), provisionally contraindicated (class 2), conditional (class 3), minimal risk (class 4), no interaction (class 5). Prediction can be carried out both for known drugs and for new, not yet synthesized substances using only their structural formulas. Created model provides an assessment of DDIs severity by prediction of different ORCA classes from the first most dangerous class to the fifth class when DDIs do not take place in the human organism. The average accuracy of DDIs class prediction is about 0.75.

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.

Discovery of new pharmaceutical substances is currently boosted by the possibility of utilization of the Synthetically Accessible Virtual Inventory (SAVI) library, which includes about 283 million molecules, each annotated with a proposed synthetic one-step route from commercially available starting materials. The SAVI database is well-suited for ligand-based methods of virtual screening to select molecules for experimental testing. In this study, we compare the performance of three approaches for the analysis of structure-activity relationships that differ in their criteria for selecting of “active” and “inactive” compounds included in the training sets. PASS (Prediction of Activity Spectra for Substances), which is based on a modified Naïve Bayes algorithm, was applied since it had been shown to be robust and to provide good predictions of many biological activities based on just the structural formula of a compound even if the information in the training set is incomplete. We used different subsets of kinase inhibitors for this case study because many data are currently available on this important class of drug-like molecules. Based on the subsets of kinase inhibitors extracted from the ChEMBL 20 database we performed the PASS training, and then applied the model to ChEMBL 23 compounds not yet present in ChEMBL 20 to identify novel kinase inhibitors. As one may expect, the best prediction accuracy was obtained if only the experimentally confirmed active and inactive compounds for distinct kinases in the training procedure were used. However, for some kinases, reasonable results were obtained even if we used merged training sets, in which we designated as inactives the compounds not tested against the particular kinase. Thus, depending on the availability of data for a particular biological activity, one may choose the first or the second approach for creating ligand-based computational tools to achieve the best possible results in virtual screening.

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/.

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.

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.

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.

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.

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.

Interaction of 2-chloro-N-(9,10-dioxo-9,10-dihydroanthracen-1-yl)acetamide with α-, β- and ω-amino acids was used to synthesize new amino-acid derivatives of 9,10-anthraquinone. Experimental testing of the antimicrobial actions of the compounds synthesized demonstrated antibacterial activity against Mycobacterium luteum and antifungal activity against Aspergillus niger and Candida tenuis. These results from experimental testing of biological activity are consistent with results obtained by predictions using the computer program PASS. The prediction identified the need for further study of the antitumor and antioxidant actions of the newly synthesized amino acid derivatives.

Background The knowledge of drug metabolite structures is essential at the early stage of drug discovery to understand the potential liabilities and risks connected with biotransformation. The determination of the site of a molecule at which a particular metabolic reaction occurs could be used as a starting point for metabolite identification. The prediction of the site of metabolism does not always correspond to the particular atom that is modified by the enzyme but rather is often associated with a group of atoms. To overcome this problem, we propose to operate with the term “reacting atom”, corresponding to a single atom in the substrate that is modified during the biotransformation reaction. The prediction of the reacting atom(s) in a molecule for the major classes of biotransformation reactions is necessary to generate drug metabolites. Results Substrates of the major human cytochromes P450 and UDP-glucuronosyltransferases from the Biovia Metabolite database were divided into nine groups according to their reaction classes, which are aliphatic and aromatic hydroxylation, N- and O-glucuronidation, N-, S- and C-oxidation, and N- and O-dealkylation. Each training set consists of positive and negative examples of structures with one labelled atom. In the positive examples, the labelled atom is the reacting atom of a particular reaction that changed adjacency. Negative examples represent non-reacting atoms of a particular reaction. We used Labelled Multilevel Neighbourhoods of Atoms descriptors for the designation of reacting atoms. A Bayesian-like algorithm was applied to estimate the structure–activity relationships. The average invariant accuracy of prediction obtained in leave-one-out and 20-fold cross-validation procedures for five human isoforms of cytochrome P450 and all isoforms of UDP-glucuronosyltransferase varies from 0.86 to 0.99 (0.96 on average). Conclusions We report that reacting atoms may be predicted with reasonable accuracy for the major classes of metabolic reactions—aliphatic and aromatic hydroxylation, N- and O-glucuronidation, N-, S- and C-oxidation, and N- and O-dealkylation. The proposed method is implemented as a freely available web service at http://www.way2drug.com/RA and may be used for the prediction of the most probable biotransformation reaction(s) and the appropriate reacting atoms in drug-like compounds.Graphical abstract. Electronic supplementary material The online version of this article (doi:10.1186/s13321-016-0183-x) contains supplementary material, which is available to authorized users.