Ligand-based virtual screening and biological evaluation of inhibitors of Mycobacterium tuberculosis H37Rv.

Novel antimycobacterial compounds are needed to expand the existing toolbox of therapeutic agents, which sometimes fail to be effective. In our study we extracted, filtered, and aggregated the diverse data on antimycobacterial activity of chemical compounds from the ChEMBL database version 24.1. These training sets were used to create the classification and regression models with PASS and GUSAR software. The IOC chemical library consisting of approximately 200,000 chemical compounds was screened using these (Q)SAR models to select novel compounds potentially having antimycobacterial activity. The QikProp tool (Schrödinger) was used to predict ADME properties and find compounds with acceptable ADME profiles. As a result, 20 chemical compounds were selected for further biological evaluation, of which 13 were the Schiff bases of isoniazid. To diversify the set of selected compounds we applied substructure filtering and selected an additional 10 compounds, none of which were Schiff bases of isoniazid. Thirty compounds selected using virtual screening were biologically evaluated in a REMA assay against the M. tuberculosis strain H37Rv. Twelve compounds demonstrated MIC below 20 µM (ranging from 2.17 to 16.67 µM) and 18 compounds demonstrated substantially higher MIC values. The discovered antimycobacterial agents represent different chemical classes.

BC CLC-Pred: a freely available web-application for quantitative and qualitative predictions of substance cytotoxicity in relation to human breast cancer cell lines.

In silico prediction of cell line cytotoxicity considerably decreases time and financial costs during drug development of new antineoplastic agents. (Q)SAR models for the prediction of drug-like compound cytotoxicity in relation to nine breast cancer cell lines (T47D, ZR-75-1, MX1, Hs-578T, MCF7-DOX, MCF7, Bcap37, MCF7R, BT-20) were created by GUSAR software based on the data from ChEMBL database (v. 30). The separate datasets related with IC50 and IG50 values were used for the creation of (Q)SAR models for each cell line. Based on leave-one-out and 5F CV procedures, 24 reasonable (Q)SAR models were selected for the creation of a freely available web-application (BC CLC-Pred: https://www.way2drug.com/bc/) to predict substance cytotoxicity in relation to human breast cancer cell lines. The mean accuracies of prediction r2, RMSE, Balance Accuracy for the selected (Q)SAR models calculated by 5F CV were 0.599, 0.679 and 0.875, respectively. As a result, BC CLC-Pred provides simultaneous quantitative and qualitative predictions of IC50 and IG50 values for most of the nine breast cancer cell lines, which may be helpful in selecting promising compounds and optimizing lead compounds during the development of new antineoplastic agents against breast cancer.

MDM-Pred: a freely available web application for predicting the metabolism of drug-like compounds by the gut microbiota.

The human gut microbiota (HGM) comprises a complex population of microorganisms that significantly affect human health, including their influence on xenobiotics metabolism. Many pharmaceuticals are taken orally and thus come into contact with HGM, which can metabolize them. Therefore, it is necessary to evaluate the effect of HGM on the fate of pharmaceuticals in the organism. We have collected information about over 600 compounds from more than eighty publications. At least half of them (329 compounds) are known to be metabolized by HGM. We have used PASS (Prediction of Activity Spectra for Substances) software to build three classification SAR models for HGM-mediated drug metabolism prediction. The first model with an accuracy of prediction 0.85 estimates whether compounds will be metabolized by HGM. The second model with an average accuracy of prediction 0.92 estimates which bacterial genera are responsible for the drug metabolism. The third model with an average accuracy of prediction 0.92 estimates the biotransformation reactions during HGM-mediated drug metabolism. The created models were used to develop the freely available web application MDM-Pred (http://www.way2drug.com/mdm-pred/).

Computer-aided Evaluation of Polyvalent Medications' Pharmacological Potential. Multiphytoadaptogen as a Case Study.

Many human diseases including cancer, degenerative and autoimmune disorders, diabetes and others are multifactorial. Pharmaceutical agents acting on a single target do not provide their efficient curation. Multitargeted drugs exhibiting pleiotropic pharmacological effects have certain advantages due to the normalization of the complex pathological processes of different etiology. Extracts of medicinal plants (EMP) containing multiple phytocomponents are widely used in traditional medicines for multifactorial disorders' treatment. Experimental studies of pharmacological potential for multicomponent compositions are quite expensive and time-consuming. In silico evaluation of EMP the pharmacological potential may provide the basis for selecting the most promising directions of testing and for identifying potential additive/synergistic effects. Multiphytoadaptogen (MPhA) containing 70 major phytocomponents of different chemical classes from 40 medicinal plant extracts has been studied in vitro, in vivo and in clinical researches. Antiproliferative and anti-tumor activities have been shown against some tumors as well as evidence-based therapeutic effects against age-related pathologies. In addition, the neuroprotective, antioxidant, antimutagenic, radioprotective, and immunomodulatory effects of MPhA were confirmed. Analysis of the PASS profiles of the biological activity of MPhA phytocomponents showed that most of the predicted anti-tumor and anti-metastatic effects were consistent with the results of laboratory and clinical studies. Antimutagenic, immunomodulatory, radioprotective, neuroprotective and anti-Parkinsonian effects were also predicted for most of the phytocomponents. Effects associated with positive effects on the male and female reproductive systems have been identified too. Thus, PASS and PharmaExpert can be used to evaluate the pharmacological potential of complex pharmaceutical compositions containing natural products.

SAR based on self consistent classifier.

The accuracy and performance of (Q)SAR models depend significantly on the data used for training. Datasets prepared on the basis of publicly available databases contain structures belonging to different chemical classes and have a highly imbalanced actives/inactives ratio. Currently, hundreds of structural descriptors are used in (Q)SAR studies. The abundance of structural descriptors gives rise to the problem of the constructed (Q)SAR models stability. The methods frequently used for the selection of a small fraction of the 'best' descriptors usually do not have sufficient mathematical justification. We propose a new approach to a self-consistent classifier for SAR analysis in order to overcome these problems. Logistic (SCLC) and extreme (SCEC) extensions of self-consistent regression (SCR) were implemented to enhance the classification capabilities of SCR. The approach was applied to classification models' development for inhibiting activity endpoints in HIV-1-related data and toxicity endpoints with subsequent fivefold cross-validation to estimate the models' performance. Comparison of the proposed SCLC and SCEC models with those developed using the original SCR and support vector machine demonstrated the comparable accuracy. Advantages in feature selection using our approach provide more generalizable (Q)SAR models. In particular, the crucial factors responsible for the observed value are determined unambiguously.

Chemical named entity recognition in the texts of scientific publications using the naïve Bayes classifier approach.

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.

[Possibilities of in silico estimations for the development of pharmaceutical composition phytoladaptogene cytotoxic for bladder cancer cells]. / Vozmozhnosti otsenok in silico dlia razrabotki farmkompozitsii fitoladaptogen, tsitotoksichnoi dlia kletok raka mochevogo puzyria.

Based on the prediction of biological activity spectra for several secondary metabolites of medicinal plants using the PASS computer program and validation in vitro of the predictions results the priority direction of the pharmaceutical composition Phytoladaptogene (PLA) development was determined. PLA is a complex of structurally diverse small organic compounds including biologically active substances of phytoadaptogenes (ginsenosides from Panax ginseng, rhodionin from Rhodiola rosea and others) compiled considering previously developed pharmaceutical compositions. Two variants of the pharmaceutical composition were studied: - the major and minor variants included 22 and 13 compounds, respectively. The probability of activity exceeds the probability of inactivity for 1400 out of 1945 pharmacological effects and mechanisms predicted by PASS for the major variant of PLA. The wide range of predicted activities is mainly due to the low structural similarity of constituent compounds. An in silico prediction indicates the possibilities of antitumor properties against bladder, stomach, colon, ovarian and cervical cancers both for minor and major PLA compositions. It was found that the highest probability values of activity were predicted for three mechanisms: apoptosis agonist, caspase-3 stimulant, and transcription factor NF-κB inhibitor. According to the PharmaExpert program they are associated with the antitumor effect against bladder cancer. Experimental validation was using the human bladder cancer cell line RT-112. The results of the MTT test have shown that the cytotoxicity of the major PLA variant is higher than that of the minor PLA variant. In vitro experiments performed using two methods (double staining with annexin V and propidium iodide and detection of active caspase-3 in cells) confirmed that the death of bladder cancer cells occurred via the apoptotic mechanism. The data obtained correspond to the results of the prediction and indicate advantages of the major PLA composition. Thus, PLA can become the basis for the development of a drug with the antitumor activity against bladder cancer. The antitumor activity predicted by PASS for other cancers may be the subject of further studies.

[Predict of metabolic stability of xenobiotics by the PASS and GUSAR programs]. / Prognoz metabolicheskoi stabil'nosti ksenobiotikov programmami PASS i GUSAR.

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).

[Modeling of drug-drug interactions between omeprazole and erythromycin with cytochrome P450 3A4 in vitro assay]. / Modelirovanie mezhlekarstvennogo vzaimodeistviia omeprazola i éritromitsina s ispol'zovaniem tsitokhrom R450-zavisimoi sistemy in vitro.

In the present study the electrochemical system based on recombinant cytochrome P450 3A4 (CYP3A4) was used for the investigation of potential drug-drug interaction between medicinal preparations employed for Helicobacter pylori eradication therapy. Drug interactions were demonstrated in association of omeprazole as a proton pump inhibitor (PPI) and macrolide antibiotic erythromycin during cytochrome P450 3A4-mediated metabolism. It was shown that in the presence of omeprazole the rate of N-demethylase activity of CYP3A4 to erythromycin measured by means of product (formaldehyde) formation decreased. Mass-spectrometry analysis of omeprazole sulfone as a CYP3A4-mediated metabolite demonstrated the absence of erythromycin influence on CYP3A4-dependent omeprazole metabolism. This phenomenon may be explained by lower spectral dissociation constant of CYP3A4-omeprazole complex (Kd = 18±2 µM) than that of CYP3A4-erythromycin complex (Kd = 52 µM). Using the electrochemical model of electrochemically-driven drug metabolism it is possible to register CYP3A4-mediated catalytic conversion of certain drugs. In vitro experiments of potential CYP3A4-mediated drug-drug interactions are in accordance with in silico modeling with program PASS and PoSMNA descriptors in the case of omeprazole/erythromycin combinations.

Improving (Q)SAR predictions by examining bias in the selection of compounds for experimental testing.

Existing data on structures and biological activities are limited and distributed unevenly across distinct molecular targets and chemical compounds. The question arises if these data represent an unbiased sample of the general population of chemical-biological interactions. To answer this question, we analyzed ChEMBL data for 87,583 molecules tested against 919 protein targets using supervised and unsupervised approaches. Hierarchical clustering of the Murcko frameworks generated using Chemistry Development Toolkit showed that the available data form a big diffuse cloud without apparent structure. In contrast hereto, PASS-based classifiers allowed prediction whether the compound had been tested against the particular molecular target, despite whether it was active or not. Thus, one may conclude that the selection of chemical compounds for testing against specific targets is biased, probably due to the influence of prior knowledge. We assessed the possibility to improve (Q)SAR predictions using this fact: PASS prediction of the interaction with the particular target for compounds predicted as tested against the target has significantly higher accuracy than for those predicted as untested (average ROC AUC are about 0.87 and 0.75, respectively). Thus, considering the existing bias in the data of the training set may increase the performance of virtual screening.

PASS-based prediction of metabolites detection in biological systems.

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 ).

Drug-drug interaction prediction using PASS.

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/).

[Discovering new antiretroviral compounds in "Big Data" chemical space of the SAVI library]. / Poisk novykh antiretrovirusnykh soedinenii v khimicheskom prostranstve "bol'shikh dannykh" biblioteki SAVI.

Despite significant advances in the application of highly active antiretroviral therapy, the development of new drugs for the treatment of HIV infection remains an important task because the existing drugs do not provide a complete cure, cause serious side effects and lead to the emergence of resistance. In 2015, a consortium of American and European scientists and specialists launched a project to create the SAVI (Synthetically Accessible Virtual Inventory) library. Its 2016 version of over 283 million structures of new easily synthesizable organic molecules, each annotated with a proposed synthetic route, were generated in silico for the purpose of searching for safer and more potent pharmacological substances. We have developed an algorithm for comparing large chemical databases (DB) based on the representation of structural formulas in SMILES codes, and evaluated the possibility of detecting new antiretroviral compounds in the SAVI database. After analyzing the intersection of SAVI with 97 million structures of the PubChem database, we found that only a small part of the SAVI (~0.015%) is represented in PubChem, which indicates a significant novelty of this virtual library. However, among those structures, 632 compounds tested for anti-HIV activity were detected, 41 of which had the desired activity. Thus, our studies for the first time demonstrated that SAVI is a promising source for the search for new anti-HIV compounds.

[Xenobiotic toxicity prediction combined with xenobiotic metabolism prediction in the human body]. / Komp'iuternaia otsenka toksichnosti ksenobiotikov s uchetom ikh metabolizma v organizme cheloveka.

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.

[Determination of Amino Acid Residues Responsible for Specific Interaction of Protein Kinases with Small Molecule Inhibitors].

Identifying amino acid positions that determine the specific interaction of proteins with small molecule ligands, is required for search of pharmaceutical targets, drug design, and solution of other biotechnology problems. We studied applicability of an original method SPrOS (specificity projection on sequence) developed to recognize functionally significant positions in amino acid sequences. The method allows residues specific to functional subgroups to be determined within the protein family based on their local surroundings in amino acid sequences. The efficiency of the method has been estimated on the protein kinase family. The residues associated with the protein specificity to inhibitors have been predicted. The results have been verified using 3D structures of protein-ligand complexes. Three small molecule inhibitors have been tested. Residues predicted with SPrOS either in contacted the inhibitor or influenced the conformation of the ligand-binding area. Excluding close homologues from the studied set makes it possible to decrease the number of difficult to interpret positions. The expediency of this procedure was determined by the relationship between an inhibitory spectrum and phylogenic partition. Thus, the method efficiency has been confirmed by matching the prediction results with the protein 3D structures.

Prediction of metabolites of epoxidation reaction in MetaTox.

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 ).

[Application of molecular descriptors for recognition of phosphorylation sites in amino acid sequences]. / Ispol'zovanie molekuliarnykh deskriptorov dlia raspoznavaniia saitov fosforilirovaniia v aminokislotnykh posledovatel'nostiakh.

Recognition of the phosphorylation sites in proteins is required for reconstruction of regulatory processes in living systems. This task is complicated because the phosphorylation motifs in amino acid sequences are considerably degenerated. To improve the prediction efficacy researchers often use additional descriptors, which should reflect physicochemical features of site-surrounding regions. We have evaluated the reasonability of this approach by applying molecular descriptors (MNA) for structural presentation of the peptide segments. Comparative testing was performed using the prognostic method PASS and two input data types: sets of the MNA descriptors represented peptides as chemical structures and amino acid sequences written using a one-letter code. Training sets were classified in accordance with the established types of the enzymes (protein kinases), modifying corresponding phosphorylation sites. The accuracy estimates obtained by prognosis validation for various classes of substrates were significantly different with both the letters and molecular descriptors. In case of the letter description, the prognosis accuracy demonstrated less dependence on the length of peptides in the training set, while in the case of structural descriptors the accuracy level was determined by the peptide size and descriptor characteristics (MNA levels). The maximal prognosis accuracy related to various kinase families was achieved at different sizes of molecular fragments covered by the MNA descriptors of corresponding levels. This obviously reflected structural differences in surroundings of phosphorylation sites modified by various protein kinases. The use of molecular descriptors provided the prognostic results comparable with the results obtained using traditional letter representation. The prognosis accuracy demonstrated less dependence on the method describing site-surrounding peptides at higher accuracy rates. Applying the MNA descriptors it is possible to achieve better accuracy in the cases when the letter description cannot provide acceptable accuracy.

[Computational prediction of human immunodeficiency resistance to reverse transcriptase inhibitors]. / Komp'iuternyi prognoz rezistentnosti virusa immunodefitsita cheloveka k ingibitoram obratnoi transkriptazy VICh.

Human immunodeficiency virus (HIV) causes acquired immunodeficiency syndrome (AIDS) and leads to over one million of deaths annually. Highly active antiretroviral treatment (HAART) is a gold standard in the HIV/AIDS therapy. Nucleoside and non-nucleoside inhibitors of HIV reverse transcriptase (RT) are important component of HAART, but their effect depends on the HIV susceptibility/resistance. HIV resistance mainly occurs due to mutations leading to conformational changes in the three-dimensional structure of HIV RT. The aim of our work was to develop and test a computational method for prediction of HIV resistance associated with the mutations in HIV RT. Earlier we have developed a method for prediction of HIV type 1 (HIV-1) resistance; it is based on the usage of position-specific descriptors. These descriptors are generated using the particular amino acid residue and its position; the position of certain residue is determined in a multiple alignment. The training set consisted of more than 1900 sequences of HIV RT from the Stanford HIV Drug Resistance database; for these HIV RT variants experimental data on their resistance to ten inhibitors are presented. Balanced accuracy of prediction varies from 80% to 99% depending on the method of classification (support vector machine, Naive Bayes, random forest, convolutional neural networks) and the drug, resistance to which is obtained. Maximal balanced accuracy was obtained for prediction of resistance to zidovudine, stavudine, didanosine and efavirenz by the random forest classifier. Average accuracy of prediction is 89%.

PASS Targets: Ligand-based multi-target computational system based on a public data and naïve Bayes approach.

Estimation of interactions between drug-like compounds and drug targets is very important for drug discovery and toxicity assessment. Using data extracted from the 19th version of the ChEMBL database ( https://www.ebi.ac.uk/chembl ) as a training set and a Bayesian-like method realized in PASS software ( http://www.way2drug.com/PASSOnline ), we developed a computational tool for the prediction of interactions between protein targets and drug-like compounds. After training, PASS Targets became able to predict interactions of drug-like compounds with 2507 protein targets from different organisms based on analysis of structure-activity relationships for 589,107 different chemical compounds. The prediction accuracy, estimated as AUC ROC calculated by the leave-one-out cross-validation and 20-fold cross-validation procedures, was about 96%. Average AUC ROC value was about 90% for the external test set from approximately 700 known drugs interacting with 206 protein targets.

[Computer evaluation of hidden potential of phytochemicals of medicinal plants of the traditional Indian ayurvedic medicine].

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.