Success Stories

Here you will find a brief overview of publications describing applications of our software. This overview provides you a guide how people use our tools, to achieve better results in their projects aimed at drug design & discovery or chemical safety assessment. Among the over 250 publications, there are many success stories about virtual screening, drug repurposing, revealing the hidden potential of natural products, chemical safety assessment, etc.

If you have some experience with the utilization of our software, please, tell us your story. Sharing of your experience, will support newcomers in their first steps to apply computer-aided drug discovery methods in practice, and will help us to improve our web-services.

General papers with references on our computational tools

"One of the first approaches in the field of in silico pharmacology was PASS (Prediction of Activity Spectra for Substances), which applies a set of 2D descriptors to compounds that are then correlated with a set of bioactivities."

Vegner L. et al. J. Med. Chem., 2013, 56: 8377. DOI: 10.1021/jm400813y

"Several ligand-based methods apply data mining methods in order to identify unknown drug−target interactions. One of the first initiatives in this field was PASS developed by Poroikov et al. (SAR & QSAR Environ. Res., 2007, 18: 101). It can predict the biological activity profile of a compound based on the analysis of structure−activity relationships for more than 250 000 biologically active substances."

Peragovics A. et al. J. Chem. Inform. Model., 2013, 53: 103. DOI: 10.1021/ci3004489

"Thorough studies have revealed pronounced differences between natural and synthetic compounds in terms of their structural and physicochemical properties, which renders the inference of targets for natural products from well-characterized drug-like compounds conceptually difficult. In fact, only a few select applications have been described." (One of the two mentioned publications is Lagunin A., Filimonov D., Poroikov V. Multi-targeted natural products evaluation based on biological activity prediction with PASS. Curr. Pharm. Des., 2010, 16: 1703 {W2D Team}).

Reker D. et al. Nature Chemistry, 2014, 6(12):1072-8. DOI: 10.1038/NCHEM.2095

"Especially in natural product research, with an abundance of possible structural scaffolds, which can interact with a huge number of pharmacological targets, a rationalized strategy for the identification of bioactivity and the discovery of leads is essential. In silico prediction tool such as PASS has proven to be especially effective in this respect."

Grienke U. et al. Phytochemistry, 2015, 114: 114-24. DOI: 10.1016/j.phytochem.2014.10.010

"In silico target prediction algorithms assess potential compound polypharmacology through the computational evaluation of the (functionally unrelated) targets modulated by a given compound, or its selectivity to species-specific targets, as they predict the probability of interaction of that compound with a panel of targets (Poroikov et al. SAR & QSAR Environ. Res., 2007, 18: 101)."

Paricharak S. et al. Journal of Cheminformatics (2015) 7:15. DOI: 10.1186/s13321-015-0063-9

"Several computer programs were used for prediction of the biological activity of 29 compounds concerning CYP1A2, 2C9, and 3A4 inhibition. PASS retrieved impressing hit rates with 75.0 %, and 72.7% for CYP1A2 and 3A4, respectively. This makes PASS extremely useful for other applications such as cherry-picking. In summary, the data suggests that if a target is predicted for a compound by PASS, this can be considered as relatively reliable; however, no conclusions should be drawn if a target is not predicted."

Kaserer T. et al. Mol. Inf. 2015, 34: 431. DOI: 10.1002/minf.201400192

"PASS predicted biological activity profiles can be further used as biological descriptors in subsequent creating (Q)SAR predictive models."

Guha R., Willighagen E. Cur. Top. Med. Chem., 2012, 12: 1946. DOI: 10.2174/1568026611212180002

"PASS is mentioned as one of the “Most cited tools for molecular docking and pharmacophore modelling."(Actually, PASS is neither docking nor pharmacophore modelling tool; it is software for prediction of biological activity profiles based on machine learning approach {W2D Team})

De La Iglesia D. et al. Cur. Top. Med. Chem., 2013, 13: 526. DOI: 10.2174/1568026611313050002
Virtual Screening

Based on PASS predictions for ~250000 molecules from the Open NCI database, we selected compounds with potential anti-angiogenesis action. Out of seven tested compounds, four showed the inhibitory activity.

Poroikov V.V. et al. J. Chem. Inform. Comput. Sci., 43(1): 228-236. DOI: 10.1021/ci020048r.

New anti-inflammatory agents possessing dual cyclooxygenase/lipoxygenase (COX/LOX) inhibition were discovered by PASS prediction of biological activity for 573 virtually designed chemical compounds. Eight tested compounds exhibited anti-inflammatory activity in the carrageenan-induced paw edema. It was shown that seven tested compounds (77.8%) were LOX inhibitors, seven compounds were COX inhibitors (77.8%), and six tested compounds (66.7%) were dual COX/LOX inhibitors.

Geronikaki A.A. et al. J. Med. Chem., 2008, 51(6): 1601–1609. DOI: 10.1021/jm701496h.

Based on PASS predictions the most promising molecules with antimicrobial activity were selected for synthesis and biological testing among the library of new 1,4-naphthoquinone aminothiazole derivatives. Antibacterial and fungicidal activities were studied using the cultures of Staphylococcus aureus, Escherichia coli and Candida tenuis microorganisms. Some of the studied compounds revealed moderate antibacterial and fungicidal activity, which in more than 90% of cases coincided with the computational predictions.

Buchkevych I. et al. Cheminė Technologija, 2012, 3(61): 62-69. DOI: 10.5755/j01.ct.61.3.2847.

"Out of 400 virtually designed cyclic nitrones, five high-priority ones were selected using the PASS program for predicting the biological activity and synthesized. Three compounds were found to be comparable to piracetam in various cognitive animal experiments."

Krasavin M. Eur. J. Med. Chem., 2015, 97: 525. DOI: 10.1016/j.ejmech.2014.11.028
Drug repurposing

In 2001 we published PASS predictions for Top200 drugs, where some novel activities for Albuterol, Amlodipine, Carisoprodol, Cisapride, Omeprazole, Oxaprosin, Ramipril, and Sertraline were suggested (Poroikov V. et al. SAR & QSAR Environ. Res., 2001, 12: 327. DOI: 10.1080/10629360108033242). In September 2014 we analyzed the published data, to identify which predictions were confirmed by the experimental studies. We found four such cases: Sertraline as a remedy for cocaine dependency treatment (Mancino M.J. et al. J. Clin. Psychopharmacol., 2014, 34: 234. DOI: 10.1097/ JCP.0000000000000062), Amlodipine as antineoplastic enhancer (moderate BCRP/ABCG2 inhibitor) (Takara K. et al. Mol. Med. Rep., 2012, 5: 603. DOI: 10.3892/mmr.2011.734), Oxaprosin as interleukin 1 antagonist (Inhibitor of production of interleukin 1β) (Rainsford K.D. et al. Inflammopharmacology, 2002, 10: 185. DOI: 10.1163/156856002321168204), Ramipril as antiarthritic agent (Shi Q. et al. Arthritis Research & Therapy Ther., 2012, 14: R223. DOI: 10.1186/ar4062).

In the same publication [Poroikov V. et al. SAR & QSAR Environ. Res., 2001, 12: 327. DOI: 10.1080/10629360108033242] Ramipril was predicted as a cognition enhancing (nootropic) agent. Later we found that this activity is also predicted for the other antihypertensive drugs of the same class (Captopril, Enalapril, Lisinopril, Perindopril, etc.). Experimental study in mice carried out in 2006 for Perindopril, Quinapril and Monopril confirmed that these compounds exhibit nootropic activity. Due to the suggested IP protection, the paper with the description of our results was published only in 2012 [Kryzhanovsky S. et al. Pharm. Chem. J., 2012, 45: 605. DOI: 10.1007/s11094-012-0689-0]. Later the cognition enhancing effect of these drugs was confirmed in clinical studies [Gao J. et al. BMJ Open, 2013, 3: e002881. DOI: 10.1136/bmjopen-2013-002881].

Drug safety & risk assessment

"Troglitazone causes severe hepatic injury in certain individuals and multiple mechanisms related to hepato-toxicity has been reported creating confusion. In the present study, the mechanism for the hepatic injury of glitazones was investigated by PASS. The results suggest that chromane containing glitazones are apoptotic agonist (activating p53 by an intrinsic pathway leading to the apoptosis) and those which do not contain the chromane are devoid of this. In case of hepato-toxicity by non-chromane glitazone and their metabolite such as M-3, RM-3, rosiglitazone and pioglitazone; PASS suggest that these chemicals are not apoptotic agonist, but they are the substrate for CYP enzyme (Phase-I Oxidative Enzyme) and Phase-II conjugating enzymes; interfering with bile acid metabolism rendering bile acid more toxic (cholestasis). This unmetabolised bile salt further initiates the process apoptosis via intrinsic and extrinsic pathway leading to the apoptosis. Immunoblot analysis further confirms our hypothesis that troglitazone (chromane containing glitazone), but not rosiglitazone and pioglitazone (non-chromane containing glitazone) increased the levels of p53 in a time-dependent manner. Hence, our prediction related to the mechanism of hepato-toxicity by apoptosis and structural insight of glitazone can be helpful in improving the drug profile of this category."

Patel H. et al. Bioorg. Med. Chem. Lett., 2015, 25: 1938. DOI: 10.1016/j.bmcl.2015.03.020

Using GUSAR (General Unrestricted Structure-Activity Relationships) we developed QSAR models for prediction of acute rodents’ toxicity for intraperitoneal, intravenous, oral and subcutaneous routes of administration [Lagunin A. et al. Molecular Informatics, 2011, 30: 241. DOI: 10.1002/minf.201000151]. The proposed approach reveals similar or higher accuracy of prediction, good coverage of the test sets and high performance in comparison with the T.E.S.T. 3.0 program used by EPA. Since our approach uses PASS predicted biological activities as parameters in QSAR models, it provides some hints on probable biochemical and physiological mechanisms of acute toxicity. Predictions of acute rat toxicity for antidiabetic vanadium-containing compounds well corresponded to the experiment [Fedorova E.V. et al. PLoS ONE, 2014, 9: e100386. DOI: 10.1371/journal.pone.0100386].

Using GUSAR, we developed and validated QSAR models for prediction of interaction of drug-like compounds with 18 antitarget proteins (13 receptors, 2 enzymes, and 3 transporters). 32 sets of end-points (IC50, Ki, and Kact) were taken into account. The proposed approach showed a reasonable accuracy of prediction for 91% of the antitarget end-points and high coverage for all external test sets [Zakharov A.V. et al. Chem. Res. Toxicol., 2012, 25: 2378. DOI: 10.1021/tx300247r].

We developed a novel in silico approach for the identification protein targets interaction, which blockade may lead to adverse reactions of drugs. This approach was applied to identification of targets associated with drug-induced myocardial infarction (DIMI). Based on statistical analysis, the 155 most significant associations between protein targets and DIMI were identified and classified into three categories of confidence: (1) high (the protein targets are known to be involved in DIMI via atherosclerotic progression; 50 targets), (2) medium (the proteins are known to participate in biological processes related with DIMI; 65 targets), and (3) low (the proteins are indirectly involved in DIMI pathogenesis; 40 proteins) [Ivanov S.M. et al. Chem. Res. Toxicol., 2014, 27: 1263. DOI: 10.1021/tx500147d].

Evaluation of hidden potential of natural products

Based on prediction of biological activity for phytoconstituents from Ayurvedic medicinal plant Ficus religiosa L. (Moraceae) the molecular mechanism of anticonvulsant action was identified. It was predicted and further confirmed by the experiment that anticonvulsant effect is caused by inhibition of GABA aminotransferase activity.

Singh D. et al. Computers in Biology and Medicine, 2014, 47: 1. DOI: 10.1016/j.compbiomed.2014.01.003

Anticancer and apoptosis‑inducing activities of the sterols identified from the soft coral Subergorgia reticulata predicted by PASS in silico have been confirmed by in vitro studies.

Byju K. et al. Pharmacognosy Magazine, 2014, 10: 65. DOI: 10.4103/0973-1296.127345

PASS predicted antibacterial and antifungal activity for some phytochemicals from Annona reticulata Linn has been confirmed by the experimental studies.

Jamkhande P.G. et al. Beni-Suef Univ. J. Basic & Appl. Sci., 2014, 3: 140. DOI: 10.1016/j.bjbas.2014.05.008

PASS predicted antioxidant and hepatoprotective activities for some phytochemicals from traditional medicinal herb Caesalpinia sappan has been confirmed by the experiment.

Kadir F.A. et al. The Scientific World Journal, 2014 (2014): Article ID 301879. DOI: 10.1155/2014/301879.
Analysis of fragments’ contribution to the activity

Using the local version of PASS, one may analyze the positive and negative impacts of different parts of molecule into the activity. Such analysis, which has been performed for natural polyphenols from olive oil, allowed to find that the chemopreventive mechanisms of action is predicted for the open dialdehydic forms of oleuropein aglycone and decarboxymethyl oleuropein aglycone, but not for their cyclic hemiacetalic isomers.

Corominas‐Faja B. et al. Aging, 2014, 6: 731.

With quantitative structure-activity models developed with GUSAR it is possible not only predict the activity for novel compound and get estimation of the applicability domain, but also to visualize the impact of particular atoms into the activity. Based on such information, new derivatives of 2-arylhydroxynitroindoles with antifungal activities were designed, synthesized and tested in fungicidal assays. Reasonable correspondence between the experimental and predicted values of antifungal activity was observed.

Kokurkina G.V. et al. Eur. J. Med. Chem., 2011, 46: 4374. DOI: 10.1016/j.ejmech.2011.07.008
Some other PASS predictions confirmed by the experiment

"Earlier unknown antiparkinsonian activity of Saxagliptin ((1S,3S,5S)-2-[(2S)-2-amino-2-(3-hydroxy-1-adamantyl) acetyl]-2-azabicyclo[3.1.0]hexane-3-carbonitrile), which has been predicted by PASS, is confirmed in a Rotenone-induced Parkinsonian Disease model."

Nassar N.A. et al. Neuropharmacology, 2015, 89: 308. DOI: 10.1016/j.neuropharm.2014.10.007

"An innovative computer-assisted approach based on the Prediction of Activity Spectra for Substances (PASS) has been applied for the discovery of new anxiolytics. An initial database comprising 5494 structures was generated by virtual combinatorial design of highly diverse chemical compounds, including different types of heterocycles such as thiazoles, pyrazoles, isatins, fused imidazoles, with the view to increase the probability of finding new chemical entities as anxiolytics. Out of the eight hits obtained from this database, four candidates were Mannich bases. … All of the candidates showed an anxiolytic effect that was comparable or greater than that of reference drug Medazepam. Mannich base 304 (R1 = NO2, R2 = C6H5) was the most potent anxiolytic in this study, being two times more potent than Medazepam."

Roman G. Eur. J. Med. Chem., 2015, 89: 743. DOI: 10.1016/j.ejmech.2014.10.076

"Using PASS, we improved the free radical scavenging capacity of BHT {Butylated Hydroxytoluene Derivatives} inhibition (25%) by more than two-fold in most compounds. improved the free radical scavenging capacity of BHT inhibition (25%) by more than two-fold in most compounds."

Arifin A. et al. Eur. J. Med. Chem., 2014, 87: 564. DOI: 10.1016/j.ejmech.2014.10.001

"According to PASS, a tool for estimation of potential pharmacological effects and biological targets, the synthesized molecules {N-alkylated C-6-isobutyl- or -propyl pyrimidine derivatives} were predicted to have antiviral activity what is in accordance with the observed antiviral activities of some C-6 substituted thymine and uracil derivates. However, different target molecules for pyrimidin-2,4-dione and 2,4-dimethoxypyrimidine derivatives were suggested as the most probable by PASS. Although both classes have shown antiproliferative effects, their modes of action at molecular level may differ according to PASS. … Predicted inhibition of cell adhesion might explain the strong antiproliferative effect observed on adherent tumor cell lines. It has already been shown that binding of tumor cells to extracellular matrix proteins might promote tumor invasiveness, that is, binding of metastatic colon cancer cells to fibrinogen or breast cancer cells to fibronectin. Adhesion analysis for adherent tumor cells on the fibronectin matrix confirmed that adhesion molecules are indeed, targeted by compound 14b. It was evident that adhesion of all tested cell lines was strongly affected by treatment with compound 14b."

Kraljevic T.G. et al. Bioorg. Med. Chem. Lett., 2014, 24: 2913. DOI: 10.1016/j.bmcl.2014.04.079

"PASS prediction of antibacterial activity of dihydropyrimidinones derivatives has been confirmed by the synthesis and biological testing against S. Aureus and S. Typhi bacteria. Moreover, PASS predicted antineoplastic action indicates the directions for further testing of the synthesized compounds."

Ramachandran V. et al., J. Chem. Biol., 2015. DOI 10.1007/s12154-015-0142-4

"A series of trifluoroethylsubstituted ureas, for which PASS predicts anticancer activity, has been synthesized and tested in the National Cancer Institute (NCI, Bethesda, USA) by the NCI-60 DTP Human Tumor Cell Line Screening Program at a single high dose (10-5 M).The moderate anticancer activity was shown against some types of cancer on the individual human cell lines for leukemia, non-small cell lung cancer and renal cancer."

Luzina E.L. and A.V. Popov. J. Fluor. Chem., 2015, 176: 82-88. DOI: 10.1016/j.jfluchem.2015.06.005

"PASS predicted antineoplastic activity for Lupane C-28-imidazolides containing 3-oxo, 3-hydroxyimino-, and 2-cyano-2,3-seco-4(23)-ene fragments in cycle A had been confirmed by synthesis and anticancer assays in vitro. The most active compound, 3-3-Hydroxyimino-lup-20(29)-en-28-yl-1H-imidazole-1-carboxylate significantly inhibited the growth and induced the death of cells of lung, colon cancer, breast, central nervous system, ovarian, prostate, renal, leukemia, and melanoma cancers. In experiments on mice, it had a moderate antineoplastic effect on inoculated breast adenocarcinoma Ca755 and large intestine adenocarcinoma AKATOL."

Kazakova O.B. et al. Rus. J. Bioorg. Chem., 2015, 41: 305. DOI: 10.1134/S1068162015020065

"The pharmacological potential of 1-(2-ethoxyethyl)-4-oktynyl-4-hydroxypiperidines was evaluated on the basis of PASS predictions. It has been found that these compounds may exhibit anesthetic, anesthetic local, spasmolytic and immunomodulatory effects. The preliminary study of 1-(2-ethoxyethyl)-4-(oktyn-1-yl)-4-propionyloxypiperidine (BIV-71) and 1-(2-ethoxyethyl)-4-(oktyn-1-yl)-4-benzoyloxypiperidine (BIV-81) activities showed that these compounds possess myelostimulatory activity exceeded those of Levamisole as a reference compound."

Iskakova T.K. et al. Procedia Chemistry, 2014, 10: 358. DOI: 10.1016/j.proche.2014.10.060
References

Ivanov S.M., Lagunin A.A., Poroikov V.V. (2015). In silico assessment of adverse drug reactions and associated mechanisms. Drug Discovery Today. Published online on 10 August 2015, DOI:10.1016/j.drudis.2015.07.018.

Giniyatyllina G.V., Smirnova I.E., Kazakova O.B., Yavorskaya N.P., Golubeva I.S., Zhukova O.S., Pugacheva R.B., Apryshko G.N., Poroikov V.V. (2015). Synthesis and anticancer activity of aminopropoxytriterpenoids. Med. Chem. Res., Published online 2 July 2015, DOI: 10.1007/s00044-015-1392-y.

Anusevicius K., Mickevicius V., Stasevych M., Zvarych V., Komarovska-Porokhnyavets O., Novikov V., Tarasova O., Gloriozova T., Poroikov V. (2015). Design, synthesis, in vitro antimicrobial activity evaluation and computational studies of new N-(4-iodophenyl)-β-alanine derivatives. Research on Chemical Intermediates. DOI: 10.1007/s11164-014-1841-0

Tarasova O.A., Urusova A.F., Filimonov D.A., Nicklaus M.C., Zakharov A.V., Poroikov V.V. (2015). QSAR Modeling Using Large-Scale Databases: Case Study for HIV-1 Reverse Transcriptase Inhibitors. Journal of Chemical Information and Modeling, 55(7), 1388-1399. DOI: 10.1021/acs.jcim.5b00019.

Ivanov S.M., Lagunin A.A., Pogodin P.V., Filimonov D.A., Poroikov V.V. (2015). Identification of drug targets related to the induction of ventricular tachyarrhythmia through systems chemical biology approach. Toxicological Sciences, 145(2): 321-336. DOI: 10.1093/toxsci/kfv054

Rudik A., Dmitriev A., Lagunin A., Filimonov D., Poroikov V. (2015). SOMP: web-service for in silico prediction of sites of metabolism for drug-like compounds. Bioinformatics, 31(12), 2046-2048. DOI: 10.1093/bioinformatics/btv087.

Goel R.K., Poroikov V., Gawande D., Lagunin A., Randhawa P., Mishra A. (2015). Revealing medicinal plants useful for comprehensive management of epilepsy and associated co-morbidities through in silico mining of their phytochemical diversity. Planta Medica, 81(6), 495-506. DOI: 10.1055/s-0035-1545884.

Dembitsky V.M., Gloriozova T.A., Poroikov V.V. (2015). Naturally occurring plant isoquinoline N-oxide alkaloids: Their pharmacological and SAR activities. Phytomedicine, 22(1), 183-202. DOI: 10.1016/j.phymed.2014.11.002.

Zvarych V.I., Stasevych M.V., Stan’ko O.V., Komarovskaya-Porokhnyavets E.Z., Poroikov V.V., Rudik A.V., Lagunin A.A., M.V. Vovk, Novikov V.P. Computerized prediction, synthesis, and antimicrobial activity of new aminoacid derivatives of 2-chloro-n-(9,10-dioxo-9,10-dihydroanthracen-1-yl)acetamide. (2014). Pharmaceutical Chemistry Journal, 48(9), 584-588.

Tretyakova E.V., Smirnova I.E., Kazakova O.B., Tolstikov G.A., Yavorskaya N.P., Golubeva I.S., Pugacheva R.B., Apryshko G.N., Poroikov V.V. (2014). Synthesis and anticancer activity of quinopimaric and maleopimaric acids’ derivatives. Bioorganic and Medicinal Chemistry, 22(22), 6481–6489.

Lagunin A.A., Goel R.K., Gawande D.Y., Priynka P., Gloriozova T.A. Dmitriev A.V., Ivanov S.M., Rudik A.V., Konova V.I., Pogodin P.V., Druzhilovsky D.S., and Poroikov V.V. (2014). Chemo- and bioinformatics resources for in silico drug discovery from medicinal plants beyond their traditional use: a critical review. Natural Product Reports, 31(11), 1585-1611. DOI: 10.1039/c4np00068d.

Ivanov S.M., Lagunin A.A., Pogodin P.V., Filimonov D.A., and Poroikov V.V. (2014). Identification of drug-induced myocardial infarction-related protein targets through the prediction of drug-target interactions and analysis of biological processes. Chemical Research in Toxicology, 27(7): 1263-1281. DOI: 10.1021/tx500147d.

Fedorova E.V., Buryakina A.V., Zakharov A.V., Filimonov D.A., Lagunin A.A., Poroikov V.V. (2014). Design, synthesis and pharmacological evaluation of novel vanadium-containing complexes as antidiabetic agents. PLoS ONE, 9(7): e100386. DOI:10.1371/journal.pone.0100386.

Filimonov D.A., Lagunin A.A., Gloriozova T.A., Rudik A.V., Druzhilovskii D.S., Pogodin P.V., Poroikov V.V. (2014). Prediction of the biological activity spectra of organic compounds using the PASS online web resource. Chemistry of Heterocyclic Compounds, 50(3), 444-457. DOI: 10.1007/s10593-014-1496-1.

Rudik A.V., Dmitriev A.V., Lagunin A.A., Filimonov D.A., Poroikov V.V. (2014). Metabolism sites prediction based on xenobiotics structural formulae and PASS prediction algorithm. Journal of Chemical Information and Modeling, 54(2), 498–507. DOI: 10.1021/ci400472j.

Singh D., Gawande D., Singh T., Poroikov V., Goel R.K. (2014). Revealing pharmacodynamics of medicinal plants using in silico approach: A case study with wet lab validation. Computers in Biology and Medicine, 47(1), 1-6. DOI: 10.1016/j.compbiomed.2014.01.003.

Raevsky O.A., Solodova S.L., Lagunin A.A., Poroikov V.V. (2013). Computer modeling of blood brain barrier permeability for physiologically active compounds. Biochemistry (Moscow) Supplement Series B: Biomedical Chemistry, 7(2), 95–107. DOI: 10.1134/S199075081302008X.

Ivanov S.M., Lagunin A.A., Zakharov A.V., Filimonov D.A., Poroikov V.V. (2013). Computer search for molecular mechanisms of ulcerogenic action of non-steroidal anti-inflammatory drugs. Biochemistry (Moscow) Supplement Series B: Biomedical Chemistry, 7(1), 40–45. DOI: 10.1134/S199075081301006X.

Lagunin A.A., Gloriozova T.A., Dmitriev A.V., Volgina N.E., Poroikov V.V. (2013). Computer Evaluation of Drug Interactions with P-Glycoprotein. Bulletin of Experimental Biology and Medicine, 154(4), 521-524. DOI: 10.1007/s10517-013-1992-9.

Choudhary K.M., Mishra A., Poroikov V.V., Goel R.K. (2013). Ameliorative effect of Curcumin on seizure severity, depression like behavior, learning and memory deficit in post-pentylenetetrazole-kindled mice. European Journal of Pharmacology, 704(1-3), 33-40. DOI: 10.1016/j.ejphar.2013.02.012.

Zakharov A.V., Lagunin A.A., Filimonov D.A., Poroikov V.V. (2012). Quantitative prediction of antitarget interaction profiles for chemical compounds. Chemical Research in Toxicology, 25(11), 2378-2385. DOI: 10.1021/tx300247r.

Filz O.A., Poroikov V.V. (2012). Design of chemical compounds with desired properties using fragment libraries. Russian Chemical Reviews, 81(2), 158-174.

Eleftheriou P., Geronikaki A., Hadjipavlou-Litina D., Vicini P., Filz O., Filimonov D., Poroikov V., Chaudhaery S.S., Roy K.K., Saxena A. (2011). Fragment-based design, docking, synthesis, biological evaluation and structure-activity relationships of 2-benzo/benzisothiazolimino-5-aryliden-4-thiazolidinones as cycloxygenase/lipoxygenase inhibitors. European Journal of Medicinal Chemistry, 2012, 47(1), 111-124.

Goel R.K., Singh D., Lagunin A., Poroikov V. (2011). PASS-assisted exploration of new therapeutic potential of natural products. Med. Chem. Res., 20(9), 1509-1514.

Kryzhanovsky S.A., Salimov R.M., Lagunin A.A., Filimonov D.A., Gloriozova T.A., Poroikov V.V. (2011). Nootropic action of some antihypertensive drugs: computational prediction and experimental testing. Pharm.-Chem. J., 45(10), 25-31.

Kokurkina G.V., Dutov M.D., Shevelev S.A., Popkov S.V., Zakharov A.V., Poroikov V.V. (2011). Synthesis, antifungal activity and QSAR study of 2-arylhydroxynitroindoles. Eur. J. Med. Chem., 46(9), 4374-4382.

Lagunin A., Zakharov A., Filimonov D., Poroikov V. (2011). QSAR Modelling of Rat Acute Toxicity on the Basis of PASS Prediction. Molecular Informatics, 30(2-3), 241-250.

Prasad Y.R., Raja Sekhar K.K., Shankarananth V., Sireesha G., Swetha Harika K., Poroikov V. (2011). Synthesis and in silico biological activity evaluation of some 1,3,5-Trisubstituted -2-pyrazolines. Journal of Pharmacy Research, 4(2), 558-560.

Lagunin A., Filimonov D.A., Poroikov V.V. (2010). Multi-targeted natural products evaluation based on biological activity prediction with PASS. Cur. Phar. Des., 16(15), 1703-1717.

Poroikov V.V., Filimonov D.A., Gloriozova T.A., Lagunin A.A., Druzhilovsky D.S., Stepanchikova A.V. (2009). Computer-aided prediction of biological activity spectra for substances: virtual chemogenomics. The Herald of Vavilov Society for Genecitists and Breeding Scientists, 13(1), 137-143 (Rus).

Lagunin A., Filimonov D., Zakharov A., Xie W., Huang Y., Zhu F., Shen T., Yao J., Poroikov V. (2009). Computer-Aided Prediction of Rodent Carcinogenicity by PASS and CISOC-PSCT. QSAR and Combinatorial Science, 28(8) 806-810.

Geronikaki A., Vicini P., Dabarakis N., Lagunin A., Poroikov V., Dearden J., Modarresi H., Hewitt M., Theophilidis G.(2009). Evaluation of the local anaesthetic activity of 3-aminobenzo[d]isothiazole derivatives using the rat sciatic nerve model. Eur. J. Med. Chem., 44(2), 473-481.

Filimonov D.A., Zakharov A.V., Lagunin A.A., Poroikov V.V. (2009). QNA based “Star Track” QSAR approach. SAR and QSAR Environ. Res., 20(7-8), 679-709.

Koborova O.N., Filimonov D.A., Zakharov A.V., Lagunin A.A., Ivanov S.M., Kel A., Poroikov V.V. (2009). In silico method for identification of promising anticancer drug targets. SAR and QSAR Environ. Res., 20(7-8), 755-766.

Filimonov D.A., Poroikov V.V. (2008). Probabilistic approach in activity prediction. In: Chemoinformatics Approaches to Virtual Screening. Eds. Alexandre Varnek and Alexander Tropsha. Cambridge (UK): RSC Publishing, p.182-216.

Geronikaki A., Druzhilovsky D., Zakharov A., Poroikov V. (2008). Computer-aided predictions for medicinal chemistry via Internet. SAR and QSAR in Environ. Res., 19(1 & 2), 27-38.

Filz O., Lagunin A., Filimonov D., Poroikov V. (2008). Computer-aided prediction of QT-prolongation. SAR and QSAR in Environ. Res., 19(1 & 2), 81-90.

Geronikaki A.A., Lagunin A.A., Hadjipavlou-Litina D.I., Elefteriou P.T., Filimonov D.A., Poroikov V.V., Alam I., Saxena A.K. (2008). Computer-aided discovery of anti-inflammatory thiazolidinones with dual cyclooxygenase/lipoxygenase inhibition. J. Med. Chem., 51(6), 1601-1609.

Poroikov V., Filimonov D., Lagunin A., Gloriozova T., Zakharov A. (2007). PASS: Identification of probable targets and mechanisms of toxicity. SAR & QSAR in Environmental Research, 18(1-2), 101-110.

Filimonov D.A., Poroikov V.V. (2006). Prediction of biological activity spectra for organic compounds. Russian Chemical Journal, 50(2), 66-75.

Poroikov V.V., Filimonov D.A., Gloriozova T.A., Lagunin A.A. (2006). Computer prediction of biological activity spectra for nitrogen-containing organic compounds. In: Nitrogen-Containing Heterocycles, M.: ICSPF, p.109-120.

Lagunin A.A., Dearden J., Filimonov D.A., Poroikov V.V. (2005). Computer-aided rodent carcinogenicity prediction. Mutation Research, 586(2), 138-146.

Poroikov V., Filimonov D. (2005). PASS: Prediction of Biological Activity Spectra for Substances. In: Predictive Toxicology. Ed. by Christoph Helma. Taylor & Francis, 459-478.

Dembitsky V.M., Gloriozova T.A., Poroikov V.V. (2005). Novel antitumor agents: marine sponge alkaloids, their synthetic analogues and derivatives. Mini-Reviews in Medicinal Chemistry, 5(3), 319-336.

Geronikaki A., Dearden J., Filimonov D., Galaeva I., Garibova T., Gloriozova T., Krajneva V., Lagunin A., Macaev F., Molodavkin G., Poroikov V., Pogrebnoi S., Shepeli F., Voronina T., Tsitlakidou M., Vlad L. (2004). Design of new cognition enhancers: from computer prediction to synthesis and biological evaluation. J. Med. Chem., 47(11), 2870-2876.

Geronikaki A., Babaev E., Dearden J., Dehaen W., Filimonov D., Galaeva I., Krajneva V., Lagunin A., Macaev F., Molodavkin G., Poroikov V., Saloutin V., Stepanchikova A., Voronina T. (2004). Design of new anxiolytics: from computer prediction to synthesis and biological evaluation. Bioorg. Med. Chem., 12(24), 6559-6568.

Poroikov V.V., Filimonov D.A., Ihlenfeldt W.-D., Gloriozova T.A., Lagunin A.A., Borodina Yu.V., Stepanchikova A.V., Nicklaus M.C. (2003). PASS Biological Activity Spectrum Predictions in the Enhanced Open NCI Database Browser. J. Chem. Inform. Comput. Sci., 43(1) 228-236.

Stepanchikova A.V., Lagunin A.A., Filimonov D.A., Poroikov V.V. (2003). Prediction of biological activity spectra for substances: Evaluation on the diverse set of drug-like structures. Current Med. Chem., 10(3), 225-233..

Lagunin A.A., Gomazkov O.A., Filimonov D.A., Gureeva T.A., Dilakyan E.A., Kugaevskaya E.V., Elisseeva Yu.E., Solovyeva N.I., Poroikov V.V. (2003). Computer-aided selection of potential antihypertensive compounds with dual mechanisms of action. J. Med. Chem., 46(15), 3326-3332.

Borodina Yu., Sadym A., Filimonov D., Blinova V., Dmitriev A., Poroikov V. (2003). Predicting biotransformation potential from molecular structure. J. Chem. Inform. Comput. Sci., 43(5), 1636-1646.

Sadym A., Lagunin A., Filimonov D., Poroikov V. (2003). Prediction of biological activity spectra via Internet. SAR and QSAR in Environmental Research, 14 (5-6), 339-347.

Geronikaki A., Lagunin A., Poroikov V., Filimonov D., Hadjipavlou-Litina D., Vicini P. (2002). Computer aided prediction of biological activity spectra: evaluating versus known and predicting of new activities for thiazole derivative. SAR and QSAR in Environmental Research, 13(3/4), 457-471.

Poroikov V.V., Filimonov D.A. (2002). How to acquire new biological activities in old compounds by computer prediction. J. Comput. Aid. Molec. Des., 16(11), 819-824.

Anzali S., Barnickel G., Cezanne B., Krug M., Filimonov D., Poroikov V. (2001). Discriminating between drugs and nondrugs by Prediction of Activity Spectra for Substances (PASS). J. Med. Chem., 44(15), 2432-2437.

Poroikov V., Akimov D., Shabelnikova E., Filimonov D. (2001). Top 200 medicines: can new actions be discovered through computer-aided prediction? SAR and QSAR in Environmental Research, 12(4), 327-344.

Poroikov V., Filimonov D. (2001). Computer-aided prediction of biological activity spectra. Application for finding and optimization of new leads. Rational Approaches to Drug Design, Eds. H.-D. Holtje, W.Sippl, Prous Science, Barcelona, p.403-407.

Lagunin A., Stepanchikova A., Filimonov D., Poroikov V. (2000). PASS: prediction of activity spectra for biologically active substances. Bioinformatics, 16(8), 747-748.

Poroikov V.V., Filimonov D.A., Borodina Yu. V., Lagunin A.A., Kos A. (2000). Robustness of biological activity spectra predicting by computer program PASS for non-congeneric sets of chemical compounds. J. Chem. Inform. Comput. Sci., 40(6), 1349-1355.

Martynova N.B., Filimonov D.A., Poroikov V.V. (2000). Computer prediction of biological activity spectrum for low molecular weight peptides and peptidomimetics. Russian Journal of Bioorganic Chemistry, 26(5), 297-305.

Geronikaki A., Poroikov V., Hadjipavlou-Litina D., Filimonov D., Lagunin A., Mgonzo R. (1999). Computer aided predicting the biological activity spectra and experimental testing of new thiazole derivatives. Quant. Struct.-Activ. Relationships, 18(1), 16-25.

Filimonov D., Poroikov V., Borodina Yu., Gloriozova T. (1999). Chemical Similarity Assessment through multilevel neighborhoods of atoms: definition and comparison with the other descriptors. J. Chem. Inf. Comput. Sci., 39:666-670.

Gloriozova T.A., Filimonov D.A., Lagunin A.A., Poroikov V.V. (1998) Evaluation of computer system for prediction of biological activity PASS on the set of new chemical compounds. Pharmaceutical Chemistry Journal, 32(12), 658-664.

Trapkov V.A., Budunova A.P., Burova O.A., Filimonov D.A., Poroikov V.V Discovery of new antiulcer agents by computer aided prediction of biological activity.(1997) Problems in Medical Chemistry (Moscow), 43(1), 41-57.

Filimonov D.A., Poroikov V.V. (1996). PASS: computerized prediction of biological activity spectra for chemical substances. In: Bioactive Compound Design: Possibilities for Industrial Use, BIOS Scientific Publishers, Oxford (UK), p.47-56.

Borodina Yu.V., Filimonov D.A., Poroikov V.V. (1996). Computer-aided prediction of prodrug activity using the PASS system. Pharmaceutical Chemistry Journal, 30(12), 760-763.

Filimonov D.A., Poroikov V.V., Karaicheva E.I. Kazarian R.K., Budunova A.P., Mikhailovskii E.M., Rudnitskikh A.V., Goncharenko L.V., Burov Yu.V. (1995) Computer-Aided Prediction of Biological Activity Spectra of Chemical Substances on the Basis of Their Structural Formulae: Computerized System PASS. Experimental and Clinical Pharmacology (Rus), 58(2), 56-62.

Poroikov V.V., Filimonov D.A., Boudunova A.P. (1993) Comparison of the Results of Prediction of the Spectra of Biological Activity of Chemical Compounds by Experts and the PASS System. Automatic Documentation and Mathematical Linguistics. Allerton Press, Inc., 27(3), 40-43.

Burov Yu.V., Poroikov V.V., Korolchenko L.V. (1990) National system for registration and biological testing of chemical compounds: facilities for new drugs search. Bull. Natl. Center for Biologically Active Compounds (Rus.), Issue 1, 4-25.

Some papers cited us

Afzal A.M., Mussa H.Y., Turner R.E., Bender A., Glen R.C. A multi-label approach to target prediction taking ligand promiscuity into account. Journal of Cheminformatics, 2015, 7(1), 24. doi: 10.1186/s13321-015-0071-9.

Chen B., Zhang T., Bond,T., Gan Y. Development of quantitative structure activity relationship (QSAR) model for disinfection byproduct (DBP) research: A review of methods and resources. Journal of Hazardous Materials, 2015, 299, 260-279. doi: 10.1016/j.jhazmat.2015.06.054.

Hähnke V.D., Bolton E.E., Bryant S.H. PubChem atom environments. Journal of Cheminformatics, 2015, 7(1), 41. doi: 10.1186/s13321-015-0076-4.

Kaserer T., Temml V., Kutil Z., Vanek T., Landa P., Schuster D. Prospective performance evaluation of selected common virtual screening tools. Case study: Cyclooxygenase (COX) 1 and 2. European Journal of Medicinal Chemistry, 2015, 96, 445-457. doi: 10.1016/j.ejmech.2015.04.017.

Jamuna S., Karthika K., Paulsamy S., Thenmozhi K., Kathiravan S., Venkatesh R. Confertin and scopoletin from leaf and root extracts of Hypochaeris radicata have anti-inflammatory and antioxidant activities. Industrial Crops and Products, 2015, 70, 221-230. doi: 10.1016/j.indcrop.2015.03.039.

Grienke U., Kaserer T., Pfluger F., Mair C.E., Langer T., Schuster D., Rollinger J.M. Accessing biological actions of Ganoderma secondary metabolites by in silico profiling. Phytochemistry, 2015, 114, 114-124. doi: 10.1016/j.phytochem.2014.10.010.

Patel H., Sonawane Y., Jagtap R., Dhangar K., Thapliyal N., Surana S., Noolvi M., Shaikh M.S., Rane R.A., Karpoormath R. Structural insight of glitazone for hepato-toxicity: Resolving mystery by PASS. Bioorganic and Medicinal Chemistry Letters, 2015, 25(9), 1938-1946. doi: 10.1016/j.bmcl.2015.03.020.

Paricharak S., Cortés-Ciriano I., Ijzerman A.P., Malliavin T.E., Bender A. Proteochemometric modelling coupled to in silico target prediction: An integrated approach for the simultaneous prediction of polypharmacology and binding affinity/potency of small molecules. Journal of Cheminformatics, 2015, 7(1), 15. doi: 10.1186/s13321-015-0063-9.

Meena P., Nemaysh V., Khatri M., Manral A., Luthra P.M., Tiwari M. Synthesis, biological evaluation and molecular docking study of novel piperidine and piperazine derivatives as multi-targeted agents to treat Alzheimer's disease. Bioorganic and Medicinal Chemistry, 2015, 23(5), 1135-1148. doi: 10.1016/j.bmc.2014.12.057.

Holliday J.D., Sani N., Willett P. Calculation of substructural analysis weights using a genetic algorithm. Journal of Chemical Information and Modeling, 2015, 55(2), 214-221. doi: 10.1021/ci500540s.

Kumar J., Dhar P., Tayade A.B., Gupta D., Chaurasia O.P., Upreti D.K., Toppo K., Arora R., Suseela M.R., Srivastava R.B. Chemical composition and biological activities of trans-Himalayan alga Spirogyra porticalis (Muell.) Cleve. PLoS ONE, 2015, 10(2), e0118255. doi: 10.1371/journal.pone.0118255.

Nassar N.N., Al-Shorbagy M.Y., Arab H.H., Abdallah D.M. Saxagliptin: A novel antiparkinsonian approach. Neuropharmacology, 2015, 89, 308-317. doi: 10.1016/j.neuropharm.2014.10.007.

Kibble M., Saarinen N., Tang J., Wennerberg K., Mäkelä S., Aittokallio T. Network pharmacology applications to map the unexplored target space and therapeutic potential of natural products. Natural Product Reports, 2015, 32(8), 1249-1266. doi: 10.1039/c5np00005j.

Ariffin A., Rahman N.A., Yehye W.A., Alhadi A.A., Kadir F.A. PASS-assisted design, synthesis and antioxidant evaluation of new butylated hydroxytoluene derivatives. European Journal of Medicinal Chemistry, 2014, 87, 564-577. doi: 10.1016/j.ejmech.2014.10.001.

Navarrete-Vázquez G., Torres-Gómez H., Hidalgo-Figueroa S., Ramírez-Espinosa J.J., Estrada-Soto S., Medina-Franco J.L., León-Rivera I., Alarcón-Aguilar F.J., Almanza-Pérez J.C. Synthesis, in vitro and in silico studies of a PPARγ and GLUT-4 modulator with hypoglycemic effect. Bioorganic and Medicinal Chemistry Letters, 2014, 24(18), 4575-4579. doi: 10.1016/j.bmcl.2014.07.068.

Gazivoda Kraljević T., Ilić N., Stepanić V., Sappe L., Petranović J., Kraljević Pavelić S., Raić-Malić S. Synthesis and in vitro antiproliferative evaluation of novel N-alkylated 6-isobutyl- and propyl pyrimidine derivatives. Bioorganic and Medicinal Chemistry Letters, 2014, 24(13), 2913-2917. doi: 10.1016/j.bmcl.2014.04.079+P152.

Zakharov A.V., Peach M.L., Sitzmann M., Nicklaus M.C. QSAR modeling of imbalanced high-throughput screening data in PubChem. Journal of Chemical Information and Modeling, 2014, 54(3), 705-712. doi: 10.1021/ci400737s.

Schilter B., Benigni R., Boobis A., Chiodini A., Cockburn A., Cronin M.T.D., Lo Piparo E., Modi S., Thiel A., Worth A. Establishing the level of safety concern for chemicals in food without the need for toxicity testing. Regulatory Toxicology and Pharmacology, 2014, 68(2), 275-296. doi: 10.1016/j.yrtph.2013.08.018.

Singh G., Bansal Y., Bansal G., Goel R.K. Design, synthesis and PASS assisted evaluation of novel 2-substituted benzimidazole derivatives as potent anthelimintics. Medicinal Chemistry, 2014, 10(4), 418-425. doi: 10.2174/157340641004140421115518.

Luzina E.L., Popov A.V. Synthesis and anticancer activity evaluation of 3,4-mono- and bicyclosubstituted N-(het)aryl trifluoromethyl succinimides. Journal of Fluorine Chemistry, 2014, 168, 121-127. doi: 10.1016/j.jfluchem.2014.09.019.

Mathew B., Suresh J., Anbazhagan S., Dev S. Proposed interaction of some novel antidepressant pyrazolines against monoamine oxidase isoforms. Molecular docking studies and PASS assisted in silico approach. Biomedicine and Aging Pathology, 2014, 4(4), 297-301. doi: 10.1016/j.biomag.2014.07.011

Schmidt F., Matter H., Hessler G., Czich A. Predictive in silico off-target profiling in drug discovery. Future Medicinal Chemistry, 2014, 6(3), 295-317. doi: 10.4155/fmc.13.202.

Corominas-Faja B., Santangelo E., Cuyàs E., Micol V., Joven J., Ariza X., Segura-Carretero A., García J., Menendez J.A. Computer-aided discovery of biological activity spectra for anti-aging and anti-cancer olive oil oleuropeins. Aging, 2014, 6(9), 731-741.

Ariffin A., Rahman N.A., Yehye W.A., Alhadi A.A., Kadir F.A. PASS-assisted design, synthesis and antioxidant evaluation of new butylated hydroxytoluene derivatives. European Journal of Medicinal Chemistry, 2014, 87, 564-577. doi: 10.1016/j.ejmech.2014.10.001.

Kadir F.A., Kassim N.M., Abdulla M.A., Yehye W.A. PASS-predicted Vitex negundo activity: Antioxidant and antiproliferative properties on human hepatoma cells-an in vitro study. BMC Complementary and Alternative Medicine, 2013, 13, 343. doi: 10.1186/1472-6882-13-343.

Scholz S., Sela E., Blaha L. et al. A European perspective on alternatives to animal testing for environmental hazard identification and risk assessment. Regulatory Toxicology and Pharmacology, 2013, 67 (3), 506-530. doi: 10.1016/j.yrtph.2013.10.003.

Peters J.-U. Polypharmacology - Foe or friend? Journal of Medicinal Chemistry, 2013, 56(22), 8955-8971. doi: 10.1021/jm400856t.

Végner L., Peragovics A., Tombor L., Jelinek B., Czobor P., Bender A., Simon Z., Málnási-Csizmadia A. Experimental confirmation of new drug-target interactions predicted by drug profile matching. Journal of Medicinal Chemistry, 2013, 56(21), 8377-8388. doi: 10.1021/jm400813y.

Differding E. Highlights from ASMC'13-advances in synthetic and medicinal chemistry-may 5-8 2013, Moscow, Russia. MedChemComm, 2013, 4(8) 1138-1144. doi: 10.1039/c3md90017g.

Mahajan D.T., Masand V.H., Patil K.N., Hadda T.B., Rastija V. Integrating GUSAR and QSAR analyses for antimalarial activity of synthetic prodiginines against multi drug resistant strain. Medicinal Chemistry Research, 2013, 22(5), 2293-2302. doi: 10.1007/s00044-012-0223-7.

Ziegler S., Pries V., Hedberg C., Waldmann H. Target identification for small bioactive molecules: Finding the needle in the haystack. Angewandte Chemie - International Edition, 2013, 52(10), 2744-2792. doi: 10.1002/anie.201208749.

Ekins S., Waller C.L., Bradley M.P., Clark A.M., Williams A.J. Four disruptive strategies for removing drug discovery bottlenecks. Drug Discovery Today, 2013, 18, 265-271. doi: 10.1016/j.drudis.2012.10.007.

Huang C., Zheng C., Li Y., Wang Y., Lu A., Yang L. Systems pharmacology in drug discovery and therapeutic insight for herbal medicines. Briefings in Bioinformatics, 2013, 15(5), 710-733. doi: 10.1093/bib/bbt035.

Valerio Jr. L.G. Predictive computational toxicology to support drug safety assessment. Methods in Molecular Biology, 2013, 930, 341-354. doi: 10.1007/978-1-62703-059-5_15.

Guha R., Willighagen E. A survey of quantitative descriptions of molecular structure. Current Topics in Medicinal Chemistry, 2012, 12(18), 1946-1956. doi: 10.2174/156802612804910278.

Raghav P.K., Verma Y.K., Gangenahalli G.U. Peptide screening to knockdown Bcl-2's anti-apoptotic activity: Implications in cancer treatment. International Journal of Biological Macromolecules, 2012, 50(3), 796–814; doi:10.1016/j.ijbiomac.2011.11.021.

Al-Rehaily A.J., Ahmad M.S., Mustafa J., Al-Oqail M.M., Hassan W., Khan S.I., Khan I.A. Solanopubamine, a rare steroidal alkaloid from Solanum schimperianum : Synthesis of some new alkyl and acyl derivatives, their anticancer and antimicrobial evaluation. Journal of Saudi Chemical Society, 2013, 17(1), 67–76; doi:10.1016/j.jscs.2011.10.003

Koutsoukas A., Simms B., Kirchmair J., Bond P.J., Whitmore A.V., Zimmer S., Young M.P., Bender A. (2011). From in silico target prediction to multi-target drug design: Current databases, methods and applications. Journal of Proteomics, 74(12), 2554-2574.

Nigsch F., Lounkine E., McCarren P., Cornett B., Glick M., Azzaoui K., Urban L., Marc P., Muller A., Hahne F., Heard D.J., Jenkins J.L. (2011). Computational methods for early predictive safety assessment from biological and chemical data. Expert Opinion on Drug Metabolism & Toxicology, 7(12), 1497-1511.

Pospieszny T., Małecka I., Paryzek Z. (2012). Synthesis and spectroscopic studies of new bile acid derivatives linked by a 1,2,3-triazole ring. Tetrahedron Letters, 53(3), 301-305.

Bello C., Dal Bello G., Cea M., Nahimana A., Aubry D., Garuti A., Motta G., Vogel P. (2011). Anti-cancer activity of 5- O -alkyl 1,4-imino-1,4-dideoxyribitols. Bioorganic & Medicinal Chemistry, 19(24), 7720-7727.

Liu P., Agrafiotis D.K., Rassokhin D.N. (2011). Power Keys: A Novel Class of Topological Descriptors Based on Exhaustive Subgraph Enumeration and their Application in Substructure Searching. J. Chem. Inf. Model., 51(11), 2843-2851.

Di Giorgio C., Benchabane Y., Boyer G., Piccerelle P., De Méo M. (2011). Evaluation of the mutagenic/clastogenic potential of 3,6-di-substituted acridines targeted for anticancer chemotherapy. Food and Chemical Toxicology, 49(11), 2773-2779.

Chand B. (2011). Structure-Bioactivity-Relationships and Crystallographic Analysis of Secondary Interactions in Pregnane-Based Steroids. J. Chem. Crystallogr., 41(12), 1901-1926.

O'Boyle N.M., Banck M., James C.A., Morley C., Vandermeersch T., Hutchison G.R. (2011). Open Babel: An open chemical toolbox. Chemistry Cenral, 3(1), 33.

Liaras K., Geronikaki A., Glamoclija J., Ciric A., Sokovic, M. (2011). Thiazole-based chalcones as potent antimicrobial agents. Synthesis and biological evaluation. Bioorganic & Medicinal Chemistry, 19 (10), 3135-3140.

Dhanachandra Singh Kh., Karthikeyan M., Kirubakaran P., Nagamani S. (2011). Pharmacophore filtering and 3D-QSAR in the discovery of new JAK2 inhibitors. Journal of Molecular Graphics and Modelling, 30, 186-197.

Kibble M., Saarinen N., Tang J., Wennerberg K., Mäkelä S., Aittokallio T. Network pharmacology applications to map the unexplored target space and therapeutic potential of natural products. Natural Product Reports, 2015, 32(8), 1249-1266. doi: 10.1039/c5np00005j.

Zydek G., Brzezinska E. (2011). Normal and reversed phase thin layer chromatography data in quantitative structure–activity relationship study of compounds with affinity for serotonin (5-HT) receptors. Journal of Chromatography B, 879(20), 1764-1772.

Verbitskiy E.V., Cheprakova E.M., Slepuhin P.A., Pervova M.G., Samorukova M.A., Zabelina O.N., Rusinov G.L., Charushin V.N. (2011). Reactions of 5-(het)aryl-1-ethyl-2(1h)-pyrazinones with terminal arylacetylenes promoted by microwave radiation. Chemistry of Heterocyclic Compounds, 47(6), 710-718.

Khurana N.I., Mohan P.S., Gajbhiye A., Goel R.K. (2011). PASS assisted prediction and pharmacological evaluation of novel nicotinic analogs for nootropic activity in mice. European Journal of Pharmacology, 662(1-3), 22-30.

Ursu O., Rayan A., Goldblum A., Oprea T.I. (2011). Understanding drug-likeness. Wiley Interdiscip. Rev.-Comput. Mol. Sci., 1(5), 760-781.

Saidemberg D.M., Baptista-Saidemberg N.B., Palma M.S. (2011).Chemometric analysis of Hymenoptera toxins and defensins: A model for predicting the biological activity of novel peptides from venoms and hemolymph. Peptides, 32(9), 1924-1933.

Singh Kh.D., Karthikeyan M., Kirubakaran P., Nagamani S. (2011). Pharmacophore filtering and 3D-QSAR in the discovery of new JAK2 inhibitors. Journal of Molecular Graphics & Modelling, 30, 186-197.

Basanagouda M., Jadhav V.B., Kulkarni M.V., Rao R. N. (2011). Computer Aided Prediction of Biological Activity Spectra: Study of Correlation between Predicted and Observed Activities for Coumarin-4-Acetic Acids. Indian Journal of Pharmaceutical Sciences, 73(1), 88-92.

Fu X., Wojak A., Neagu D., Ridley M., Travis K. (2011). Data governance in predictive toxicology: A review. Chemistry Cenral, 3(1), 24.

Vasilevsky S.F., Govdi A.I., Sorokina I.V., Tolstikova T.G., Baev D.S., Tolstikov G.A., Mamatuyk V.I., Alabugin I.V. (2011). Rapid access to new bioconjugates of betulonic acid via click chemistry. Bioorganic & Medicinal Chemistry Letters, 21(1), 62-65.

Samwald M., Jentzsch A., Bouton C., Kallesøe C.S., Willighagen E., Hajagos J., Marshall M.S., Stephens S. (2011). Linked open drug data for pharmaceutical research and development. Chemistry Cenral, 3(1), 19.

Willighagen E.L., Brändle M.P. (2011). Resource description framework technologies in chemistry. Chemistry Cenral, 3(1), 15.

Panico A.M., Vicini P., Geronikaki A., Incerti M.C., Venera C., Lucia M.R., Ronsisvalle S. (2011). Heteroarylimino-4-thiazolidinones as inhibitors of cartilage degradation. Bioorganic Chemistry, 39(1), 48-52.

Ghadimi S., Asad-Samani K., Ebrahimi-Valmoozi A.A. (2011). Synthesis, Spectroscopic Characterization and Structure-Activity Relationship of Some Phosphoramidothioate Pesticides. J. Iran Chem. Soc., 8(3), 717-726.

Bieler M., Heilker R., Koeppen H., Schneider G. (2011). Assay Related Target Similarity (ARTS) - Chemogenomics Approach for Quantitative Comparison of Biological Targets. J. Chem Inf. Model., 51(8), 1897-1905.

Potikha L.M., Turelyk A.R., Kovtunenko V.A. (2011). Synthesis of azepino[1,2-a]benzimidazoles and imidazo[1,2-a]azepines. Chem. Heterocycl. Compds., 47(6), 745-754.

Hubert L., Jr., Lin Y., Dion V., Wilson J.H. (2011). Topoisomerase 1 and Single-Strand Break Repair Modulate Transcription-Induced CAG Repeat Contraction in Human. Cells.Mol. Cell. Biol., 31(15), 3105-3112

Ojha P.K., Mitra I., Das R.N., Roy K. (2011). Further exploring r m 2 metrics for validation of QSPR models. Chemometrics and Intelligent Laboratory Systems, 107(1), 194-205.

Ke S.Y., Cao X.F. (2011). Substituted-nicotinyl thiourea derivatives bearing pyrimidine moiety: synthesis and biological evaluation. Res. Chem. Intermed., 37(6), 627-633.

 Singh D., Singh B., Goel R.K. (2011). Traditional uses, phytochemistry and pharmacology of Ficus religiosa : A review. Journal of Ethnopharmacology, 134(3), 565-583.

Ekins S., Williams A.J., Krasowski M.D., Freundlich J.S. (2011). In silico repositioning of approved drugs for rare and neglected diseases. Drug Discovery Today, 16(7-8), 298-310.

Gozalbes R., Pineda-Lucena A. (2011). Small Molecule Databases and Chemical Descriptors Useful in Chemoinformatics: An Overview. Combinatorial Chemistry & High Throughput Screening, 14 (6), 548-558.

Pontiki E., Hadjipavlou-Litina D., Litinas K., Nicolotti O., Carotti A. (2011). Design, synthesis and pharmacobiological evaluation of novel acrylic acid derivatives acting as lipoxygenase and cyclooxygenase-1 inhibitors with antioxidant and anti-inflammatory activities. European Journal of Medicinal Chemistry, 46 (1), 191-200.

Zydek G., Brzezinska E. (2011). Normal and reversed phase thin layer chromatography data in quantitative structure-activity relationship study of compounds with affinity for serotonin (5-HT) receptors. J. Chromatogr. B., 879(20), 1764-1772.

Taboureau O., Jorgensen F.S. (2011). In Silico Predictions of hERG Channel Blockers in Drug Discovery: From Ligand-Based and Target-Based Approaches to Systems Chemical Biology. Chem. High Throughput Screen., 14(5), 375-387.

Liaras K., Geronikaki A., Glamoclija J., Ciric A., Sokovic M. (2011). Thiazole-based chalcones as potent antimicrobial agents. Synthesis and biological evaluation. Bioorg. Med. Chem., 19(10), 3135-3140.

Tian R., Liu Z.-M., Jin H.-W., Zhang L.-R., Lin W.-H. (2011). Target Identification of Isomalabaricane Terpenes Extracted from Sponges. Acta Phys.-Chim. Sin., 27(5), 1214-1222.

Navarrete-Vazquez G., Torres-Gomez H., Guerrero-Alvarez J., Tlahuext H. (2011). Synthesis and Crystal Structure of Ethyl 2-[4-(acetylamino)phenoxy]-2-methylpropanoate, A Potential Anti-inflammatory and Antidyslipidemic Hybrid. J. Chem. Crystallogr., 41(5), 732-736.

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