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The developers of PASS
added a number of new tools and publish them on
Way2Drug.com
The list of publications
given below tells you about new developments on the way2drug platform .
1. Ivanov S.M., Lagunin A.A.,
Rudik A.V., Filimonov D.A., Poroikov V.V. (2017). ADVERPred – web
service for prediction of adverse effects of drugs. Journal of Chemical
Information and Modeling, DOI:
https://doi.org/10.1021/acs.jcim.7b00568
2. Lagunin A., Rudik A.,
Filimonov D., Druzhilovsky D., Poroikov V. (2017). ROSC-Pred:
web-service for rodent organ-specific carcinogenicity prediction.
Bioinformatics. DOI:
https://doi.org/10.1093/bioinformatics/btx678
3. Janardhan S, Konova V.,
Lagunin A., Rao B.V., Sastry G.N., Poroikov V. (2017). Recent Advances
in the development of pharmaceutical agents for metabolic disorders: a
computational perspective. Curr. Med. Chem. DOI:
https://doi.org/10.2174/0929867324666171002120647
4. Nagamani S., Gaur A.S.,
Tanneeru K., Muneeswaran G., Madugula S.S., MPDS Consortium,
Druzhilovskiy D., Poroikov V.V., Sastry G.N. (2017) Molecular property
diagnostic suite (MPDS): Development of disease-specific open source web
portals for drug discovery. SAR and QSAR in Environmental Research, 28
(11), 913-926. DOI:
https://doi.org/10.1080/1062936X.2017.1402819
5. Tarasova O, Rudik A,
Dmitriev A, Lagunin A, Filimonov D, Poroikov V. (2017). QNA-Based
Prediction of Sites of Metabolism. Molecules, 22 (12), 2123. DOI:
https://doi.org/10.3390/molecules22122123
6. Janardhan S., John L.,
Prasanthi M., Poroikov V.V., Sastry G.N. (2017). A QSAR and molecular
modeling study towards new lead finding: Polypharmacological approach to
Mycobacterium tuberculosis. SAR and QSAR in Environmental Research, 28
(10), 815-832. DOI:
https://doi.org/10.1080/1062936X.2017.1398782
7. Murtazalieva K.A.,
Druzhilovskiy D.S., Goel R.K., Sastry G.N., Poroikov V.V. (2017). How
good are publicly available web services that predict bioactivity
profiles for drug repurposing? SAR and QSAR in Environmental Research,
28 (10), 843-862. DOI:
https://doi.org/10.1080/1062936X.2017.1399448
8. Druzhilovskiy D.S., Rudik
A.V., Filimonov D.A., Gloriozova T.A., Lagunin A.A., Dmitriev A.V.,
Pogodin P.V., Dubovskaja V.I., Ivanov S.M., Tarasova O.A., Bezhentsev
V.M., Murtazalieva K.A., Semin M.I., Mayorov I.S., Gaur A.S., Sastry G.N.,
and Poroikov V.V. (2017). Computational platform Way2Drug: from the
prediction of biological activity to drug repurposing. Russian Chemical
Bulletin, International Edition, 66 (10), 1832-1841.
9. Dmitriev A., Rudik A.,
Filimonov D., Lagunin A., Pogodin P., Druzhilovsky D., Ivanov S.,
Tarasova O., Konova V., Bezhentsev V., Poroikov V. (2017). Integral
estimation of xenobiotics toxicity with regard to their metabolism in
human organism. Pure and Applied Chemistry, 89 (10), 1449-1458. DOI:
https://doi.org/10.1515/pac-2016-1205
10. Ivanov S., Semin M.,
Lagunin A., Filimonov D., Poroikov V. (2017). In silico identification
of proteins associated with drug-induced liver injury based on the
prediction of drug-target interactions. Mol. Inform., 36 (7), 1600142.
DOI:
https://doi.org/10.1002/minf.201600142
11. Stasevych M., Zvarych
V., Lunin V., Deniz N.G., Gokmen Z., Akgun O., Ulukaya E., Poroikov V.,
Gloriozova T., Novikov V. (2017). Computer-aided prediction and
experimental testing of the dithiocarbamate derivatives of
9,10-anthracenedione as anticancer agents. SAR & QSAR Environ. Res., 28
(5), 355-366. DOI:
https://doi.org/10.1080/1062936X.2017.1323796
12. Rudik A.V., Bezhentsev
V.M., Dmitriev A.V., Druzhilovskiy D.S., Lagunin A.A., Filimonov D.A.,
Poroikov V.V. (2017). MetaTox: Web Application for Predicting Structure
and Toxicity of Xenobiotics Metabolites. Journal of Chemical Information
and Modeling, 57 (4), 638–642. DOI:
https://doi.org/10.1021/acs.jcim.6b00662
13.Tarasova O., Filimonov D.,
Poroikov V. (2017). PASS-based approach to predict HIV-1 reverse
transcriptase resistance. J. Bioinform. Comput. Biol., 15 (2), 1650040-1
- 1650040-15. DOI:
http://dx.doi.org/10.1142/S0219720016500402
14. Bezhentsev V.M.,
Druzhilovskii D.S., Ivanov S.M., Filimonov D.A., Sastry G.N., Poroikov
V.V. (2017). Web Resources for Discovery and Development of New
Medicines. Pharm. Chem. J., 51 (2), 91–99. DOI:
https://doi.org/10.1007/s11094-017-1563-x
15. Gawande D.Y.,
Druzhilovsky D., Gupta R.C., Poroikov V., Goel R.K. (2017).
Anticonvulsant activity and acute neurotoxic profile of Achyranthes
aspera Linn. Journal of Ethnopharmacology, 202 (18) 97-102. DOI:
https://doi.org/10.1016/j.jep.2017.03.018
16. Dembitsky V.M.,
Gloriozova T.A., Poroikov V.V. (2017). Pharmacological and predicted
activities of natural azo compounds. Nat. Prod. Bioprospect., 7, 151.
DOI:
https://doi.org/10.1007/s13659-016-0117-3
17. Rudik A.V., Dmitriev A.V.,
Lagunin A.A., Filimonov D.A., Poroikov V.V. (2016). Prediction of
reacting atoms for the major biotransformation reactions of organic
xenobiotics. J. Cheminform., 8, 68. DOI:
https://doi.org/10.1186/s13321-016-0183-x
18. Bezhentsev V.M.,
Tarasova O.A., Dmitriev A.V., Rudik A.V., Lagunin A.A., Filimonov D.A.,
Poroikov V.V. Computer-aided prediction of xenobiotics metabolism in the
human organism. Russ. Chem. Rev., 2016, 85 (8) 854-879.
19. Zakharov A.V., Varlamova
E.V., Lagunin A.A., Dmitriev A.V., Muratov E.N., Fourches D., Kuz'min
V.E., Poroikov V.V., Tropsha A., Nicklaus M.C. (2016). QSAR Modeling and
Prediction of Drug-Drug Interactions. Molecular Pharmaceutics, 13 (2),
545–556. DOI:
https://doi.org/10.1021/acs.molpharmaceut.5b00762
20. Druzhilovskiy D.S.,
Rudik A.V., Filimonov D.A., Lagunin A.A., Gloriozova T.A., and Poroikov
V.V. (2016). Online resources for the prediction of biological activity
of organic compounds. Russian Chemical Bulletin, International Edition,
65 (2), 384-393. DOI:
https://doi.org/10.1007/s11172-016-1310-6
21. Ivanov S.M., Lagunin A.A.,
Poroikov V.V. (2016). In silico assessment of adverse drug reactions and
associated mechanisms. Drug Discovery Today, 21 (1), 58-71. DOI:
https://doi.org/10.1016/j.drudis.2015.07.018
22. Guasch L., Zakharov A.V.,
Tarasova O.A., Poroikov V.V., Liao C., Nicklaus M.C. (2016). Novel HIV-1
integrase inhibitor development by virtual screening based on QSAR
models. Current Topics in Medicinal Chemistry, 16 (4), 441-448. DOI:
https://doi.org/10.2174/1568026615666150813150433
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