Recent Progress in Machine Learning-based Prediction  of Peptide Activity for Drug Discovery

Qihui       Wu; Hanzhong       Ke; Dongli       Li; Qi       Wang; Jiansong       Fang; Jingwei       Zhou

Abstract

Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.

Keywords: Antimicrobial peptides (AMPs), Anticancer peptides (ACPs), Anti-inflammatory peptides (AIPs), Machine learning, Activity prediction, R&D.

« Previous Next »

Graphical Abstract

[1] 
Mócsai, A.; Kovács, L.; Gergely, P. What is the future of targeted therapy in rheumatology: Biologics or small molecules? BMC Med.,  2014, 12(1), 43-51.
[http://dx.doi.org/10.1186/1741-7015-12-43] [PMID: 24620738] 
[2] 
Fosgerau, K.; Hoffmann, T. Peptide therapeutics: Current status and future directions. Drug Discov. Today,  2015, 20(1), 122-128.
[http://dx.doi.org/10.1016/j.drudis.2014.10.003] [PMID: 25450771] 
[3] 
Castel, G.; Chtéoui, M.; Heyd, B.; Tordo, N. Phage display of combinatorial peptide libraries: application to antiviral research. Molecules,  2011, 16(5), 3499-3518.
[http://dx.doi.org/10.3390/molecules16053499] [PMID: 21522083] 
[4] 
de la Torre, B.G.; Albericio, F. The pharmaceutical industry in 2017. An analysis of FDA drug approvals from the perspective of molecules. Molecules,  2018, 23(3), 533-540.
[http://dx.doi.org/10.3390/molecules23030533] [PMID: 29495494] 
[5] 
Du, Q.S.; Xie, N.Z.; Huang, R.B. Recent development of peptide drugs and advance on theory and methodology of peptide inhibitor design. Med. Chem.,  2015, 11(3), 235-247.
[http://dx.doi.org/10.2174/1573406411666141229163355] [PMID: 25548931] 
[6] 
Fang, J.; Yang, R.; Gao, L.; Yang, S.; Pang, X.; Li, C.; He, Y.; Liu, A.L.; Du, G.H. Consensus models for CDK5 inhibitors in silico and their application to inhibitor discovery. Mol. Divers.,  2015, 19(1), 149-162.
[http://dx.doi.org/10.1007/s11030-014-9561-3] [PMID: 25511641] 
[7] 
Fang, J.; Li, Y.; Liu, R.; Pang, X.; Li, C.; Yang, R.; He, Y.; Lian, W.; Liu, A.L.; Du, G.H. Discovery of multitarget-directed ligands against Alzheimer’s disease through systematic prediction of chemical-protein interactions. J. Chem. Inf. Model.,  2015, 55(1), 149-164.
[http://dx.doi.org/10.1021/ci500574n] [PMID: 25531792] 
[8] 
Fang, J.; Yang, R.; Gao, L.; Zhou, D.; Yang, S.; Liu, A.L.; Du, G.H. Predictions of BuChE inhibitors using support vector machine and naive Bayesian classification techniques in drug discovery. J. Chem. Inf. Model.,  2013, 53(11), 3009-3020.
[http://dx.doi.org/10.1021/ci400331p] [PMID: 24144102] 
[9] 
Shah, Y.; Sehgal, D.; Valadi, J.K. Recent trends in antimicrobial peptide prediction using machine learning techniques. Bioinformation,  2017, 13(12), 415-416.
[http://dx.doi.org/10.6026/97320630-013415] [PMID: 29379261] 
[10] 
Porto, W.F.; Pires, A.S.; Franco, O.L. Computational tools for exploring sequence databases as a resource for antimicrobial peptides. Biotechnol. Adv.,  2017, 35(3), 337-349.
[http://dx.doi.org/10.1016/j.biotechadv.2017.02.001] [PMID: 28216008] 
[11] 
Liu, S.; Fan, L.; Sun, J.; Lao, X.; Zheng, H. Computational resources and tools for antimicrobial peptides. J. Pept. Sci.,  2017, 23(1), 4-12.
[http://dx.doi.org/10.1002/psc.2947] [PMID: 27966-278] 
[12] 
Torrent, M.; Nogués, M.V.; Boix, E. Discovering new in silico tools for antimicrobial peptide prediction. Curr. Drug Targets,  2012, 13(9), 1148-1157.
[http://dx.doi.org/10.2174/138945012-802002311] [PMID: 22664076] 
[13] 
Wang, Z.; Wang, G. APD: The antimicrobial peptide database. Nucleic Acids Res.,  2004, 32(Database issue), D590-D592.
[http://dx.doi.org/10.1093/nar/gkh025] [PMID: 14681488] 
[14] 
Wang, G.; Li, X.; Wang, Z. APD2: The updated antimicrobial peptide database and its application in peptide design. Nucleic Acids Res.,  2009, 37(Database issue), D933-D937.
[http://dx.doi.org/10.1093/nar/gkn823] [PMID: 18957441] 
[15] 
Whitmore, L.; Wallace, B.A. The peptaibol database: A database for sequences and structures of naturally occurring peptaibols. Nucleic Acids Res.,  2004, 32, D593-D594.
[http://dx.doi.org/10.1093/nar/gkh077] [PMID: 14681489] 
[16] 
Fjell, C.D.; Hancock, R.E.; Cherkasov, A. AMPer: A database and an automated discovery tool for antimicrobial peptides. Bioinformatics,  2007, 23(9), 1148-1155.
[http://dx.doi.org/10.1093/bioinformatics/btm068] [PMID: 17341497] 
[17] 
Seebah, S.; Suresh, A.; Zhuo, S.; Choong, Y.H.; Chua, H.; Chuon, D.; Beuerman, R.; Verma, C. Defensins knowledgebase: a manually curated database and information source focused on the defensins family of antimicrobial peptides. Nucleic Acids Res.,  2007, 35(Database issue), D265-D268.
[http://dx.doi.org/10.1093/nar/gkl866] [PMID: 17090586] 
[18] 
Wang, C.K.L.; Kaas, Q.; Chiche, L.; Craik, D.J. CyBase: a database of cyclic protein sequences and structures, with applications in protein discovery and engineering. Nucleic Acids Res.,  2008, 36(Database issue), D206-D210.
[PMID: 17986451] 
[19] 
Hammami, R.; Ben Hamida, J.; Vergoten, G.; Fliss, I. PhytAMP: A database dedicated to antimicrobial plant peptides. Nucleic Acids Res.,  2009, 37, D963-D968.
[http://dx.doi.org/10.1093/nar/gkn655] [PMID: 18836196] 
[20] 
Thomas, S.; Karnik, S.; Barai, R.S.; Jayaraman, V.K.; Idicula-Thomas, S. CAMP: A useful resource for research on antimicrobial peptides. Nucleic Acids Res.,  2010, 38(Database issue), D774-D780.
[http://dx.doi.org/10.1093/nar/gkp1021] [PMID: 19923233] 
[21] 
Waghu, F.H.; Barai, R.S.; Gurung, P.; Idicula-Thomas, S. CAMPR3: A database on sequences, structures and signatures of antimicrobial peptides. Nucleic Acids Res.,  2016, 44(D1), D1094-D1097.
[http://dx.doi.org/10.1093/nar/gkv1051] [PMID: 26467-475] 
[22] 
Seshadri Sundararajan, V.; Gabere, M.N.; Pretorius, A.; Adam, S.; Christoffels, A.; Lehväslaiho, M.; Archer, J.A.; Bajic, V.B. DAMPD: A manually curated antimicrobial peptide database. Nucleic Acids Res.,  2012, 40, D1108-D1112.
[http://dx.doi.org/10.1093/nar/gkr1063] [PMID: 22110032] 
[23] 
Piotto, S.P.; Sessa, L.; Concilio, S.; Iannelli, P. YADAMP: Yet another database of antimicrobial peptides. Int. J. Antimicrob. Agents,  2012, 39(4), 346-351.
[http://dx.doi.org/10.1016/j.ijanti-micag.2011.12.003] [PMID: 22325123] 
[24] 
Novković, M.; Simunić, J.; Bojović, V.; Tossi, A.; Juretić, D. DADP: The database of anuran defense peptides. Bioinformatics,  2012, 28(10), 1406-1407.
[http://dx.doi.org/10.1093/bioinformatics/bts141] [PMID: 22467909] 
[25] 
Gautam, A.; Chaudhary, K.; Singh, S.; Joshi, A.; Anand, P.; Tuknait, A.; Mathur, D.; Varshney, G.C.; Raghava, G.P.S. Hemolytik: A database of experimentally determined hemolytic and non-hemolytic peptides. Nucleic Acids Res.,  2014, 42(Database issue), D444-D449.
[http://dx.doi.org/10.1093/nar/gkt1008] [PMID: 24174543] 
[26] 
Qureshi, A.; Thakur, N.; Tandon, H.; Kumar, M. AVPdb: a database of experimentally validated antiviral peptides targeting medically important viruses. Nucleic Acids Res.,  2014, 42(Database issue), D1147-D1153.
[http://dx.doi.org/10.1093/nar/gkt1191] [PMID: 24285301] 
[27] 
Pirtskhalava, M.; Gabrielian, A.; Cruz, P.; Griggs, H.L.; Squires, R.B.; Hurt, D.E.; Grigolava, M.; Chubinidze, M.; Gogoladze, G.; Vishnepolsky, B.; Alekseyev, V.; Rosenthal, A.; Tartakovsky, M. DBAASP v.2: An enhanced database of structure and antimicrobial/cytotoxic activity of natural and synthetic peptides. Nucleic Acids Res.,  2016, 44(D1), D1104-D1112.
[http://dx.doi.org/10.1093/nar/gkv1174] [PMID: 26578581] 
[28] 
Singh, S.; Chaudhary, K.; Dhanda, S.K.; Bhalla, S.; Usmani, S.S.; Gautam, A.; Tuknait, A.; Agrawal, P.; Mathur, D.; Raghava, G.P.S. SATPdb: A database of structurally annotated therapeutic peptides. Nucleic Acids Res.,  2016, 44(D1), D1119-D1126.
[http://dx.doi.org/dx.doi. org/10.1093/nar/gkv1114] [PMID: 26527728] 
[29] 
Fan, L.; Sun, J.; Zhou, M.; Zhou, J.; Lao, X.; Zheng, H.; Xu, H. DRAMP: A comprehensive data repository of antimicrobial peptides. Sci. Rep.,  2016, 6, 24482-24488.
[http://dx.doi.org/10.1038/srep24482] [PMID: 27075512] 
[30] 
Tyagi, A.; Tuknait, A.; Anand, P.; Gupta, S.; Sharma, M.; Mathur, D.; Joshi, A.; Singh, S.; Gautam, A.; Raghava, G.P.S. CancerPPD: a database of anticancer peptides and proteins. Nucleic Acids Res.,  2015, 43, D837-D843.
[http://dx.doi.org/10.1093/nar/gku892] [PMID: 25270878] 
[31] 
Deng, L.; Hinton, G.; Kingsbury, B. In New types of deep neural network learning for speech recognition and related applications: An overview. IEEE International Conference on Acoustics, Speech and Signal Processing,  2013, pp. 8599-8603.
[http://dx.doi.org/10.1109/ICASSP.2013.6639344] 
[32] 
LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature,  2015, 521(7553), 436-444.
[http://dx.doi.org/10.1038/nature14539] [PMID: 26017442] 
[33] 
Yap, C.W. PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J. Comput. Chem.,  2011, 32(7), 1466-1474.
[http://dx.doi.org/10.1002/jcc.21707] [PMID: 21425294] 
[34] 
Tetko, I.V.; Gasteiger, J.; Todeschini, R.; Mauri, A.; Livingstone, D.; Ertl, P.; Palyulin, V.A.; Radchenko, E.V.; Zefirov, N.S.; Makarenko, A.S.; Tanchuk, V.Y.; Prokopenko, V.V. Virtual computational chemistry laboratory--design and description. J. Comput. Aided Mol. Des.,  2005, 19(6), 453-463.
[http://dx.doi.org/10.1007/s10822-005-8694-y] [PMID: 16231203] 
[35] 
Fang, J.; Pang, X.C.; Yan, R.; Lian, W.; Li, C.; Wang, Q.; Liu, A.L.; Du, G. Discovery of neuroprotective compounds by machine learning approaches. RSC Advances,  2016, 6(12), 9857-9871.
[http://dx.doi.org/10.1039/C5RA23035G] 
[36] 
Frank, E.; Hall, M.; Trigg, L.; Holmes, G.; Witten, I.H. Data mining in bioinformatics using Weka. Bioinformatics,  2004, 20(15), 2479-2481.
[http://dx.doi.org/10.1093/bioinformatics/bth261] [PMID: 15073010] 
[37] 
Demšar, J.; Curk, T.; Erjavec, A.; Goru, Č.; Hočevar, T.; Milutinovič, M.; Možina, M.; Polajnar, M.; Toplak, M.; Starič, A. Orange: Data Mining Toolbox in Python. J. Mach. Learn. Res.,  2013, 14(1), 2349-2353.
[38] 
Rao, H. B.; Zhu, F.; Yang, G. B.; Li, Z. R.; Chen, Y. Z. Update of
PROFEAT: A web server for computing structural and physicochemical
features of proteins and peptides from amino acid sequence. Nucleic Acids Res.,  2011, 39, (Web Server issue), W385-
W390.
[http://dx.doi.org/10.1093/nar/gkr284] 
[39] 
Sharma, B.K. Analysis and prediction of antibacterial peptides. BMC Bioinformatics,  2007, 8(1), 1-10.
[http://dx.doi.org/10.1186/1471-2105-8-1] [PMID: 17199892] 
[40] 
Lata, S.; Mishra, N.K.; Raghava, G.P. AntiBP2: improved version of antibacterial peptide prediction. BMC Bioinformatics,  2010, 11(Suppl. 1), S19.
[http://dx.doi.org/10.1186/1471-2105-11-S1-S19] [PMID: 20122190] 
[41] 
Waghu, F.H.; Gopi, L.; Barai, R.S.; Ramteke, P.; Nizami, B.; Idicula-Thomas, S. CAMP: Collection of sequences and structures of antimicrobial peptides. Nucleic Acids Res.,  2014, 42(Database issue), D1154-D1158.
[http://dx.doi.org/10.1093/nar/gkt1157] [PMID: 24265220] 
[42] 
Dziuba, B.; Dziuba, M. New milk protein-derived peptides with potential antimicrobial activity: An approach based on bioinformatic studies. Int. J. Mol. Sci.,  2014, 15(8), 14531-14545.
[http://dx.doi.org/10.3390/ijms150814531] [PMID: 25141106] 
[43] 
Wang, P.; Hu, L.; Liu, G.; Jiang, N.; Chen, X.; Xu, J.; Zheng, W.; Li, L.; Tan, M.; Chen, Z.; Song, H.; Cai, Y.D.; Chou, K.C. Prediction of antimicrobial peptides based on sequence alignment and feature selection methods. PLoS One,  2011, 6(4), e18476.
[http://dx.doi.org/10.1371/journal.pone.0018476] [PMID: 21533231] 
[44] 
Friedman, J.H.; Baskett, F.; Shustek, L.J. An algorithm for finding nearest neighbors. IEEE Trans. Comput.,  1975, C-24(10), 1000-1006.
[http://dx.doi.org/10.1109/T-C.1975.224110] 
[45] 
Thakur, N.; Qureshi, A.; Kumar, M. AVPPred: Collection and
prediction of highly effective antiviral peptides. Nucleic Acids
Res,  2012, 40, (Web Server issue), W199-W204.
[http://dx.doi.org/10.1093/nar/gks450] 
[46] 
Porto, W.F.; Pires, Á.S.; Franco, O.L. CS-AMPPred: An updated SVM model for antimicrobial activity prediction in cysteine-stabilized peptides. PLoS One,  2012, 7(12), e51444.
[http://dx.doi.org/ dx.doi.org/10.1371/journal.pone.0051444] [PMID: 23240023] 
[47] 
Joseph, S.; Karnik, S.; Nilawe, P.; Jayaraman, V.K.; Idicula-Thomas, S. ClassAMP: a prediction tool for classification of antimicrobial peptides. IEEE/ACM Trans. Comput. Biol. Bioinformatics,  2012, 9(5), 1535-1538.
[http://dx.doi.org/10.1109/TCBB.2012.89] [PMID: 22732690] 
[48] 
Niarchou, A.; Alexandridou, A.; Athanasiadis, E.; Spyrou, G. C-PAmP: large scale analysis and database construction containing high scoring computationally predicted antimicrobial peptides for all the available plant species. PLoS One,  2013, 8(11), e79728.
[http://dx.doi.org/10.1371/journal.pone.0079728] [PMID: 24244550] 
[49] 
Xiao, X.; Wang, P.; Lin, W.Z.; Jia, J.H.; Chou, K.C. iAMP-2L: A two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Anal. Biochem.,  2013, 436(2), 168-177.
[http://dx.doi.org/10.1016/j.ab.2013.01.019] [PMID: 23395824] 
[50] 
Tanford, C. Contribution of hydrophobic interactions to the stability of the globular conformation of proteins. J. Am. Chem. Soc.,  1962, 84(22), 4240-4247.
[http://dx.doi.org/10.1021/ja00881a009] 
[51] 
Gaydon, A.G. Handbook of chemistry and physics. Soil Science
Society of America Journal.,  (47th Ed.), 1967, 18(4), pp. 115.
[52] 
Higgins, M.J.P.  Data for biochemical research. Biochemical Society
Transactions., (3rd. ) 1987, 15(4), pp. 777.2-777.
[53] 
Chou, K.C. Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins,  2001, 43(3), 246-255.
[http://dx.doi.org/10.1002/prot.1035] [PMID: 11288174] 
[54] 
Lee, H.T.; Lee, C.C.; Yang, J.R.; Lai, J.Z.; Chang, K.Y. A large-scale structural classification of antimicrobial peptides. BioMed Res. Int.,  2015, 2015, 475062-475067.
[PMID: 26000295] 
[55] 
Meher, P.K.; Sahu, T.K.; Saini, V.; Rao, A.R. Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC. Sci. Rep.,  2017, 7, 42362-42373.
[http://dx.doi.org/10.1038/srep42362] [PMID: 28205576] 
[56] 
Veltri, D.; Kamath, U.; Shehu, A. Improving recognition of antimicrobial peptides and target selectivity through machine learning and genetic programming. IEEE/ACM Trans. Comput. Biol. Bioinformatics,  2017, 14(2), 300-313.
[http://dx.doi.org/10.1109/TCBB.2015.2462364] [PMID: 28368808] 
[57] 
Vishnepolsky, B.; Gabrielian, A.; Rosenthal, A.; Hurt, D.E.; Tartakovsky, M.; Managadze, G.; Grigolava, M.; Makhatadze, G.I.; Pirtskhalava, M. Predictive model of linear AMPs active against gram-negative bacteria. J. Chem. Inf. Model.,  2018, 58(5), 1141-1151.
[http://dx.doi.org/10.1021/acs.jcim.8b00118] [PMID: 29716188] 
[58] 
Mader, J.S.; Hoskin, D.W. Cationic antimicrobial peptides as novel cytotoxic agents for cancer treatment. Expert Opin. Investig. Drugs,  2006, 15(8), 933-946.
[http://dx.doi.org/10.1517/13543784. 15.8.933] [PMID: 16859395] 
[59] 
Gupta, S.; Sharma, A.K.; Shastri, V.; Madhu, M.K.; Sharma, V.K. Prediction of anti-inflammatory proteins/peptides: An in silico approach. J. Transl. Med.,  2017, 15(1), 7-17.
[http://dx.doi.org/10.1186/s12967-016-1103-6] [PMID: 28057002] 
[60] 
Nagpal, G.; Usmani, S.S.; Dhanda, S.K.; Kaur, H.; Singh, S.; Sharma, M.; Raghava, G.P. Computer-aided designing of immunosuppressive peptides based on IL-10 inducing potential. Sci. Rep.,  2017, 7, 42851-42860.
[http://dx.doi.org/10.1038/srep42851] [PMID: 28211521] 
[61] 
Hawrylowicz, C.M.; O’Garra, A. Potential role of interleukin-10-secreting regulatory T cells in allergy and asthma. Nat. Rev. Immunol.,  2005, 5(4), 271-283.
[http://dx.doi.org/10.1038/nri1589] [PMID: 15775993] 
[62] 
Bromberg, J.S. IL-10 immunosuppression in transplantation. Curr. Opin. Immunol.,  1995, 7(5), 639-643.
[http://dx.doi.org/10.1016/0952-7915(95)80070-0] [PMID: 8573306] 
[63] 
Shinozaki, K.; Yahata, H.; Tanji, H.; Sakaguchi, T.; Ito, H.; Dohi, K. Allograft transduction of IL-10 prolongs survival following orthotopic liver transplantation. Gene Ther.,  1999, 6(5), 816-822.
[http://dx.doi.org/10.1038/sj.gt.3300881] [PMID: 10505106] 
[64] 
Manavalan, B.; Shin, T.H.; Kim, M.O.; Lee, G. AIPpred: Sequence-based prediction of anti-inflammatory peptides using random forest. Front. Pharmacol.,  2018, 9, 276.
[http://dx.doi.org/10.3389/fphar.2018.00276] [PMID: 29636690] 
[65] 
Tyagi, A.; Kapoor, P.; Kumar, R.; Chaudhary, K.; Gautam, A.; Raghava, G.P. In silico models for designing and discovering novel anticancer peptides. Sci. Rep.,  2013, 3(10), 2984-2991.
[http://dx.doi.org/10.1038/srep02984] [PMID: 24136089] 
[66] 
Vijayakumar, S.; Ptv, L. ACPP: A web server for prediction and design of anti-cancer peptides. Int. J. Pept. Res. Ther.,  2015, 21(1), 99-106.
[http://dx.doi.org/10.1007/s10989-014-9435-7] 
[67] 
Chen, W.; Ding, H.; Feng, P.; Lin, H.; Chou, K.C. iACP: A sequence-based tool for identifying anticancer peptides. Oncotarget,  2016, 7(13), 16895-16909.
[http://dx.doi.org/10.18632/oncotarget.7815] [PMID: 26942877] 
[68] 
Manavalan, B.; Basith, S.; Shin, T.H.; Choi, S.; Kim, M.O.; Lee, G. MLACP: Machine-learning-based prediction of anticancer peptides. Oncotarget,  2017, 8(44), 77121-77136.
[http://dx.doi.org/10.18632/oncotarget.20365] [PMID: 29100375] 
[69] 
Hajisharifi, Z.; Piryaiee, M.; Mohammad Beigi, M.; Behbahani, M.; Mohabatkar, H. Predicting anticancer peptides with Chou’s pseudo amino acid composition and investigating their mutagenicity via Ames test. J. Theor. Biol.,  2014, 341, 34-40.
[http://dx.doi.org/10.1016/j.jtbi.2013.08.037] [PMID: 24035842] 
[70] 
Akbar, S.; Hayat, M.; Iqbal, M.; Jan, M.A. iACP-GAEnsC: Evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space. Artif. Intell. Med.,  2017, 79, 62-70.
[http://dx.doi.org/10.1016/j.artmed.2017.06.008] [PMID: 28655440] 
[71] 
Kabir, M.; Hayat, M. iRSpot-GAEnsC: Identifing recombination spots via ensemble classifier and extending the concept of Chou’s PseAAC to formulate DNA samples. Mol. Genet. Genomics,  2016, 291(1), 285-296.
[http://dx.doi.org/10.1007/s00438-015-1108-5] [PMID: 26319782] 
[72] 
Iqbal, M.; Hayat, M. “iSS-Hyb-mRMR”: Identification of splicing sites using hybrid space of pseudo trinucleotide and pseudo tetranucleotide composition. Comput. Methods Programs Biomed.,  2016, 128, 1-11.
[http://dx.doi.org/10.1016/j.cmpb.2016.02.006] [PMID: 27040827] 
[73] 
Wang, P.; Ge, R.; Liu, L.; Xiao, X.; Li, Y.; Cai, Y. Multi-label learning for predicting the activities of antimicrobial peptides. Sci. Rep.,  2017, 7(1), 2202-2212.
[http://dx.doi.org/10.1038/s41598-017-01986-9] [PMID: 28526820] 
[74] 
Bhadra, P.; Yan, J.; Li, J.; Fong, S.; Siu, S.W.I. AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest. Sci. Rep.,  2018, 8(1), 1697-1706.
[http://dx.doi.org/10.1038/s41598-018-19752-w] [PMID: 29374199] 
[75] 
Veltri, D.; Kamath, U.; Shehu, A. Deep learning improves antimicrobial peptide recognition. Bioinformatics,  2018, 34(16), 2740-2747.
[http://dx.doi.org/10.1093/bioinformatics/bty179] [PMID: 29590297] 
[76] 
Xu, L.; Liang, G.; Wang, L.; Liao, C. A novel hybrid sequence-based model for identifying anticancer peptides. Genes (Basel),  2018, 9(3), e158.
[http://dx.doi.org/10.3390/genes9030158] [PMID: 29534013] 
[77] 
Fernandes, F.C.; Rigden, D.J.; Franco, O.L. Prediction of antimicrobial peptides based on the adaptive neuro-fuzzy inference system application. Biopolymers,  2012, 98(4), 280-287.
[http://dx.doi.org/10.1002/bip.22066] [PMID: 23193592] 
[78] 
Jang, R. Adaptive network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern.,  1993, 23(3), 665-683.
[http://dx.doi.org/10.1109/21.256541] 
[79] 
Ng, X.Y.; Rosdi, B.A.; Shahrudin, S. Prediction of antimicrobial peptides based on sequence alignment and support vector machine-pairwise algorithm utilizing LZ-complexity. BioMed Res. Int.,  2015, 2015(3), 212715-212727.
[PMID: 25802839] 
[80] 
Abboud, G.; Kaplowitz, N. Drug-induced liver injury. Drug Saf.,  2007, 30(4), 277-294.
[http://dx.doi.org/10.2165/00002018-200730040-00001] [PMID: 17408305] 
[81] 
Li, W.; Godzik, A. Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics,  2006, 22(13), 1658-1659.
[http://dx.doi.org/10.1093/bioinformatics/btl158] [PMID: 16731699] 
[82] 
Fang, J.; Liu, C.; Wang, Q.; Lin, P.; Cheng, F. In silico polypharmacology of natural products. Brief. Bioinform., 2017.
[http://dx.doi.org/10.1093/bib/bbx045] [PMID: 28460068] 
[83] 
Yamanishi, Y.; Araki, M.; Gutteridge, A.; Honda, W.; Kanehisa, M. Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics,  2008, 24(13), i232-i240.
[http://dx.doi.org/10.1093/bioinformatics/btn162] [PMID: 18586719] 
[84] 
Cheng, F.; Zhou, Y.; Li, J.; Li, W.; Liu, G.; Tang, Y. Prediction of chemical-protein interactions: Multitarget-QSAR versus computational chemogenomic methods. Mol. Biosyst.,  2012, 8(9), 2373-2384.
[http://dx.doi.org/10.1039/c2mb25110h] [PMID: 22751809] 
[85] 
Wen, M.; Zhang, Z.; Niu, S.; Sha, H.; Yang, R.; Yun, Y.; Lu, H. Deep-learning-based drug-target interaction prediction. J. Proteome Res.,  2017, 16(4), 1401-1409.
[http://dx.doi.org/10.1021/acs.jproteome.6b00618] [PMID: 28264154] 
[86] 
Wang, L.; You, Z.H.; Chen, X.; Xia, S.X.; Liu, F.; Yan, X.; Zhou, Y.; Song, K.J. A Computational-based method for predicting drug-target interactions by using stacked autoencoder deep neural network. J. Comput. Biol.,  2017, 25(3), 361-373.
[87] 
Cai, C.; Fang, J.; Guo, P.; Wang, Q.; Hong, H.; Moslehi, J.; Cheng, F. In Silico pharmacoepidemiologic evaluation of drug-induced cardiovascular complications using combined classifiers. J. Chem. Inf. Model.,  2018, 58(5), 943-956.
[http://dx.doi.org/10.1021/acs.jcim.7b00641] [PMID: 29712429] 
[88] 
Xu, Y.; Dai, Z.; Chen, F.; Gao, S.; Pei, J.; Lai, L. Deep learning for drug-induced liver injury. J. Chem. Inf. Model.,  2015, 55(10), 2085-2093.
[http://dx.doi.org/10.1021/acs.jcim.5b00238] [PMID: 26437739] 

Rights & Permissions Print Cite

Article Metrics

102

17

1

Journal Information

For Authors

For Editors

For Reviewers

Explore Articles

Open Access

Open Access Articles

For Visitors

DOI https://dx.doi.org/10.2174/1568026619666190122151634	Print ISSN 1568-0266
Publisher Name Bentham Science Publisher	Online ISSN 1873-4294

Current Topics in Medicinal Chemistry

Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

Abstract

Graphical Abstract

Chemistry Based on Natural Products for Therapeutic Purposes

Current Trends in Drug Discovery Based on Artificial Intelligence and Computer-Aided Drug Design

Drug Discovery in the Age of Artificial Intelligence

From Biodiversity to Chemical Diversity: Focus of Flavonoids

Current Topics in Medicinal Chemistry

Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

Abstract

Graphical Abstract

Call for Papers in Thematic Issues

Chemistry Based on Natural Products for Therapeutic Purposes

Current Trends in Drug Discovery Based on Artificial Intelligence and Computer-Aided Drug Design

Drug Discovery in the Age of Artificial Intelligence

From Biodiversity to Chemical Diversity: Focus of Flavonoids

Related Journals

Related Books

Related Articles