Generic placeholder image

Combinatorial Chemistry & High Throughput Screening

Editor-in-Chief

ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

Editorial

Computational Models and Methods for Drug Target Prediction and Drug Repositioning

Author(s): Guohua Huang

Volume 23, Issue 4, 2020

Page: [270 - 273] Pages: 4

DOI: 10.2174/138620732304200409112209

[1]
Adams, C.P.; Brantner, V.V. Estimating the cost of new drug development: is it really 802 million dollars? Health Aff. (Millwood), 2006, 25(2), 420-428.
[http://dx.doi.org/10.1377/hlthaff.25.2.420] [PMID: 16522582]
[2]
Huang, G.; Li, J.; Zhao, C. Computational prediction and analysis of associations between small molecules and binding-associated S-nitrosylation sites. Molecules, 2018, 23(4), 954.
[http://dx.doi.org/10.3390/molecules23040954] [PMID: 29671802]
[3]
Gu, W.; Miller, S.; Chiu, C.Y. Clinical metagenomic next-generation sequencing for pathogen detection. Annu. Rev. Pathol., 2019, 14, 319-338.
[http://dx.doi.org/10.1146/annurev-pathmechdis-012418-012751] [PMID: 30355154]
[4]
Mardis, E.R. The impact of next-generation sequencing on cancer genomics: from discovery to clinic. Cold Spring Harb. Perspect. Med., 2019, 9(9), a036269
[http://dx.doi.org/10.1101/cshperspect.a036269] [PMID: 30397020]
[5]
Ameur, A.; Kloosterman, W.P.; Hestand, M.S. Single-molecule sequencing: towards clinical applications. Trends Biotechnol., 2019, 37(1), 72-85.
[http://dx.doi.org/10.1016/j.tibtech.2018.07.013] [PMID: 30115375]
[6]
Battich, N.; Beumer, J.; de Barbanson, B.; Krenning, L.; Baron, C.S.; Tanenbaum, M.E.; Clevers, H.; van Oudenaarden, A. Sequencing metabolically labeled transcripts in single cells reveals mRNA turnover strategies. Science, 2020, 367(6482), 1151-1156.
[http://dx.doi.org/10.1126/science.aax3072] [PMID: 32139547]
[7]
Grinnan, D.; Trankle, C.; Andruska, A.; Bloom, B.; Spiekerkoetter, E. Drug repositioning in pulmonary arterial hypertension: challenges and opportunities. Pulm. Circ., 2019, 9(1), 2045894019832226
[http://dx.doi.org/10.1177/2045894019832226] [PMID: 30729869]
[8]
Xuan, P.; Cao, Y.; Zhang, T.; Wang, X.; Pan, S.; Shen, T. Drug repositioning through integration of prior knowledge and projections of drugs and diseases. Bioinformatics, 2019, 35(20), 4108-4119.
[http://dx.doi.org/10.1093/bioinformatics/btz182] [PMID: 30865257]
[9]
Zeng, X.; Zhu, S.; Liu, X.; Zhou, Y.; Nussinov, R.; Cheng, F. deepDR: a network-based deep learning approach to in silico drug repositioning. Bioinformatics, 2019, 35(24), 5191-5198.
[http://dx.doi.org/10.1093/bioinformatics/btz418] [PMID: 31116390]
[10]
Turanli, B.; Altay, O.; Borén, J.; Turkez, H.; Nielsen, J.; Uhlen, M.; Arga, K.Y.; Mardinoglu, A. Systems biology based drug repositioning for development of cancer therapy. Semin. Cancer Biol., 2019. E-pub ahead of print
[http://dx.doi.org/10.1016/j.semcancer.2019.09.020] [PMID: 31568815]
[11]
Daryaee, F.; Tonge, P.J. Pharmacokinetic-pharmacodynamic models that incorporate drug-target binding kinetics. Curr. Opin. Chem. Biol., 2019, 50, 120-127.
[http://dx.doi.org/10.1016/j.cbpa.2019.03.008] [PMID: 31030171]
[12]
IJzerman, A.P.; Guo, D. Drug-target association kinetics in drug discovery. Trends Biochem. Sci., 2019, 44(10), 861-871.
[http://dx.doi.org/10.1016/j.tibs.2019.04.004] [PMID: 31101454]
[13]
Nguyen, P.A.; Born, D.A.; Deaton, A.M.; Nioi, P.; Ward, L.D. Phenotypes associated with genes encoding drug targets are predictive of clinical trial side effects. Nat. Commun., 2019, 10, 1-11.
[14]
Miranda, M.R.; Sayé, M.M. Chagas disease treatment: from new therapeutic targets to drug discovery and repositioning. Curr. Med. Chem., 2019, 26(36), 6517-6518.
[http://dx.doi.org/10.2174/092986732636191202125919] [PMID: 31849285]
[15]
Parisi, D.; Adasme, M.F.; Sveshnikova, A.; Moreau, Y.; Schroeder, M. Drug repositoning or target repositioning: a structural perspective of drug-target-indication relationship for available repurposed drugs. bioRxiv, 2019. [Pre-print article].
[http://dx.doi.org/10.1101/715094]
[16]
Che, J.; Chen, L.; Guo, Z-H.G.; Wang, S. Aorigele, Drug target group prediction with multiple drug networks. Comb. Chem. High Throughput Screen., 2020, 23(4), 274-285.
[http://dx.doi.org/10.2174/1386207322666190702103927] [PMID: 31267864]
[17]
Kuhn, M.; Szklarczyk, D.; Pletscher-Frankild, S.; Blicher, T.H.; von Mering, C.; Jensen, L.J.; Bork, P. STITCH 4: integration of protein-chemical interactions with user data. Nucleic Acids Res., 2014, 42(Database issue), D401-D407.
[http://dx.doi.org/10.1093/nar/gkt1207] [PMID: 24293645]
[18]
Cho, H.; Berger, B.; Peng, J. Compact integration of multi-network topology for functional analysis of genes. Cell Syst., 2016, 3, 540-548.
[http://dx.doi.org/10.1016/j.cels.2016.10.017]
[19]
Cortes, C.; Vapnik, V. Support-vector networks. Mach. Learn., 1995, 20, 273-297.
[http://dx.doi.org/10.1007/BF00994018]
[20]
Lee, S.; Lee, K.H.; Song, M.; Lee, D. Building the process-drug–side effect network to discover the relationship between biological processes and side effects. BMC Bioinformatics, 2011, 12, S2.
[http://dx.doi.org/10.1186/1471-2105-12-S2-S2]
[21]
Pauwels, E.; Stoven, V.; Yamanishi, Y. Predicting drug side-effect profiles: a chemical fragment-based approach. BMC Bioinformatics, 2011, 12, 169.
[http://dx.doi.org/10.1186/1471-2105-12-169] [PMID: 21586169]
[22]
Zhou, B.; Zhao, X.; Lu, J.; Sun, Z.; Liu, M.; Zhou, Y.; Liu, R.; Wang, Y. Relating substructures and side effects of drugs with chemical-chemical interactions. Comb. Chem. High Throughput Screen., 2020, 23(4), 285-294.
[http://dx.doi.org/10.2174/1386207322666190702102752] [PMID: 31267865]
[23]
Crişan, A.M. BădeliŢă, S.N.; Jardan, C.; Vasilache, E.D.; Dobrea, C.M.; Gheorghe, A.; Tălmaci, R.; Arion, C.V.; Bardaş, A.; Găman, A.M.; Coriu, D. The occurrence of chronic lymphocytic leukemia after chronic phase of chronic myeloid leukemia: case report and literature review. Rom. J. Morphol. Embryol., 2015, 56(3), 1145-1151.
[PMID: 26662151]
[24]
Nagao, T.; Takahashi, N.; Kameoka, Y.; Noguchi, S.; Shinohara, Y.; Ohyagi, H.; Kume, M.; Sawada, K. Dasatinib-responsive chronic lymphocytic leukemia in a patient treated for coexisting chronic myeloid leukemia. Intern. Med., 2013, 52(22), 2567-2571.
[http://dx.doi.org/10.2169/internalmedicine.52.0392] [PMID: 24240798]
[25]
Lu, J.; Zhang, Y-H.; Wang, S.; Bi, Y.; Huang, T.; Luo, X.; Cai, Y-D. Analysis of four types of leukemia using gene ontology term and kyoto encyclopedia of genes and genomes pathway enrichment scores. Comb. Chem. High Throughput Screen., 2020, 23(4), 295-303.
[http://dx.doi.org/10.2174/1386207322666181231151900] [PMID: 30599106]
[26]
Harris, M.A.; Clark, J.; Ireland, A.; Lomax, J.; Ashburner, M.; Foulger, R.; Eilbeck, K.; Lewis, S.; Marshall, B.; Mungall, C.; Richter, J.; Rubin, G.M.; Blake, J.A.; Bult, C.; Dolan, M.; Drabkin, H.; Eppig, J.T.; Hill, D.P.; Ni, L.; Ringwald, M.; Balakrishnan, R.; Cherry, J.M.; Christie, K.R.; Costanzo, M.C.; Dwight, S.S.; Engel, S.; Fisk, D.G.; Hirschman, J.E.; Hong, E.L.; Nash, R.S.; Sethuraman, A.; Theesfeld, C.L.; Botstein, D.; Dolinski, K.; Feierbach, B.; Berardini, T.; Mundodi, S.; Rhee, S.Y.; Apweiler, R.; Barrell, D.; Camon, E.; Dimmer, E.; Lee, V.; Chisholm, R.; Gaudet, P.; Kibbe, W.; Kishore, R.; Schwarz, E.M.; Sternberg, P.; Gwinn, M.; Hannick, L.; Wortman, J.; Berriman, M.; Wood, V.; de la Cruz, N.; Tonellato, P.; Jaiswal, P.; Seigfried, T.; White, R. The gene ontology (GO) database and informatics resource. Nucleic Acids Res., 2004, 32(Database issue), D258-D261.
[PMID: 14681407]
[27]
Kanehisa, M. The KEGG database. Novartis Found Symp, 2002, 247, 91-101. discussion 101-103, 119-128, 244-152.
[http://dx.doi.org/10.1002/0470857897.ch8]
[28]
Peng, H.; Long, F.; Ding, C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27(8), 1226-1238.
[http://dx.doi.org/10.1109/TPAMI.2005.159] [PMID: 16119262]
[29]
Xiang, Q.; Feng, K.; Liao, B.; Liu, Y.; Huang, G. Prediction of lysine malonylation sites based on pseudo amino acid. Comb. Chem. High Throughput Screen., 2017, 20(7), 622-628.
[http://dx.doi.org/10.2174/1386207320666170314102647] [PMID: 28292251]
[30]
Abdel-Hafiz, H.A.; Horwitz, K.B. Post-translational modifications of the progesterone receptors. J. Steroid Biochem. Mol. Biol., 2014, 140, 80-89.
[http://dx.doi.org/10.1016/j.jsbmb.2013.12.008] [PMID: 24333793]
[31]
Du, Y.; Cai, T.; Li, T.; Xue, P.; Zhou, B.; He, X.; Wei, P.; Liu, P.; Yang, F.; Wei, T. Lysine malonylation is elevated in type 2 diabetic mouse models and enriched in metabolic associated proteins. Mol. Cell. Proteomics, 2015, 14(1), 227-236.
[http://dx.doi.org/10.1074/mcp.M114.041947] [PMID: 25418362]
[32]
Wang, S.; Li, J.; Sun, X.; Zhang, Y-H.; Huang, T.; Cai, Y. Computational method for identifying malonylation sites by using random forest algorithm. Comb. Chem. High Throughput Screen., 2020, 23(4), 304-312.
[http://dx.doi.org/10.2174/1386207322666181227144318] [PMID: 30588879]
[33]
Breiman, L. Random forests. Mach. Learn., 2001, 45, 5-32.
[http://dx.doi.org/10.1023/A:1010933404324]
[34]
Maninis, K-K.; Pont-Tuset, J.; Arbeláez, P.; Van Gool, L. Deep retinal image understanding. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, 2016, pp. 140-148.
[35]
Patton, N.; Aslam, T.M.; MacGillivray, T.; Deary, I.J.; Dhillon, B.; Eikelboom, R.H.; Yogesan, K.; Constable, I.J. Retinal image analysis: concepts, applications and potential. Prog. Retin. Eye Res., 2006, 25(1), 99-127.
[http://dx.doi.org/10.1016/j.preteyeres.2005.07.001] [PMID: 16154379]
[36]
Zhang, X.; Chen, W.; Li, G.; Li, W. The use of texture features to extract and analyze useful information from retinal images. Comb. Chem. High Throughput Screen., 2019, 23, 313-318.
[http://dx.doi.org/10.2174/1386207322666191022123445] [PMID: 31642771]
[37]
Fatma, N.; Singh, D.P.; Shinohara, T.; Chylack, L.T. Jr Transcriptional regulation of the antioxidant protein 2 gene, a thiol-specific antioxidant, by lens epithelium-derived growth factor to protect cells from oxidative stress. J. Biol. Chem., 2001, 276(52), 48899-48907.
[http://dx.doi.org/10.1074/jbc.M100733200] [PMID: 11677226]
[38]
Lobo, V.; Patil, A.; Phatak, A.; Chandra, N. Free radicals, antioxidants and functional foods: Impact on human health. Pharmacogn. Rev., 2010, 4(8), 118-126.
[http://dx.doi.org/10.4103/0973-7847.70902] [PMID: 22228951]
[39]
Tong, H.; Zhang, X.; Meng, X.; Lu, L.; Mai, D.; Qu, S. Simvastatin inhibits activation of NADPH oxidase/p38 MAPK pathway and enhances expression of antioxidant protein in Parkinson disease models. Front. Mol. Neurosci., 2018, 11, 165.
[http://dx.doi.org/10.3389/fnmol.2018.00165] [PMID: 29872377]
[40]
Xu, Y.; Wen, Y.; Han, G. Antioxidant proteins identification based on support vector machine. Comb. Chem. High Throughput Screen., 2020, 23, 319-325.
[http://dx.doi.org/10.2174/1386207323666200306125538] [PMID: 32141416]
[41]
Reich, D.E.; Schaffner, S.F.; Daly, M.J.; McVean, G.; Mullikin, J.C.; Higgins, J.M.; Richter, D.J.; Lander, E.S.; Altshuler, D. Human genome sequence variation and the influence of gene history, mutation and recombination. Nat. Genet., 2002, 32(1), 135-142.
[http://dx.doi.org/10.1038/ng947] [PMID: 12161752]
[42]
Mills, R.E.; Luttig, C.T.; Larkins, C.E.; Beauchamp, A.; Tsui, C.; Pittard, W.S.; Devine, S.E. An initial map of insertion and deletion (INDEL) variation in the human genome. Genome Res., 2006, 16(9), 1182-1190.
[http://dx.doi.org/10.1101/gr.4565806] [PMID: 16902084]
[43]
Li, N.; Yang, J.; Zhu, W.; Liang, Y. MVSC: a multi-variation simulator of cancer genome. Comb. Chem. High Throughput Screen., 2020, 23(4), 326-333.

© 2024 Bentham Science Publishers | Privacy Policy