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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.

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.

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.

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.

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.

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.

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.

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.