Scalable Prediction of Compound-protein Interaction on Compressed Molecular Fingerprints.
Compound-protein interaction prediction
data compression
drug discovery
Journal
Molecular informatics
ISSN: 1868-1751
Titre abrégé: Mol Inform
Pays: Germany
ID NLM: 101529315
Informations de publication
Date de publication:
01 2020
01 2020
Historique:
received:
24
09
2019
accepted:
07
12
2019
pubmed:
8
1
2020
medline:
22
12
2020
entrez:
8
1
2020
Statut:
ppublish
Résumé
Prediction of compound-protein interactions with fingerprints has recently become challenging in recent pharmaceutical science for an efficient drug discovery. We review two scalable methods for predicting drug-protein interactions on fingerprints. Especially, we introduce two techniques of learning statistical models using lossless and lossy data compressions. The first one is a method using a trie representation of fingerprints which enables us to learn predictive models on the compressed format. The second one is a method using lossy data compression called feature maps (FMs). Recently, quite a few numbers of FMs for kernel approximations have been proposed and minwise hashing, one method of this kind. has been applied to predictions of compound-protein interactions and shows an effectiveness of the method. Overall, we show learning statistical models on the compressed format is effective for predicting compound-protein interactions on a large-scale.
Identifiants
pubmed: 31908150
doi: 10.1002/minf.201900130
doi:
Substances chimiques
Pharmaceutical Preparations
0
Proteins
0
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1900130Informations de copyright
© 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
Références
S. Whitebread, J. Hamon, D. Bojanic, L. Urban, Drug Dicov. Today 2015, 10, 1421-1433.
C. R. Chong, D. J. Chong, Nature 2007, 448, 645-6.
A. Rahimi, B. Recht, Proc. Int. 21st Neural Information Processing Systems, 2007, 1177-1184.
A. Broder, M. Charikar, A. Frieze, J. Comp. Syst. Sci. 2000, 60, 630-659.
G. Jacobson, 30th Annu. Sym. Found. Comp. Sci. 1089, 549-554.
Y. Tabei, E. Pauwels, V. Stoven, K. Takemoto, Y. Yamanishi, Bioinformatics 2012, 28, 487-497.
H. Iwata, S. Mizutani, Y. Tabei, M. Kotera, S. Goto, Y. Yamanishi, BMC Syst. Biol. 2013, 7, 18.
Y. Tabei, M. Kotera, R. Sawada, Y. Yamanishi, BMC Syst. Biol. 2019, 13, 39.
H. Yabuuchi, S. Niijima, H. Takematsu, T. Ida, T. Hirokawa, T. Hara, T. Ogawa, Y. Minowa, G. Tsujijmoto, Y. Okuno, Mol. Syst. Biol. 2011, 7, 472.
R. E. Fan, K. W. Chang, C. J. Lin, J. Machine Learning Research 2008, 8, 1871-1874.
G. Andrew, J. Gao, Proc. 24th Int. Conf. Machine Learning 2007, 33-40.
D. C. Liu, D. C. Liu, J. Nocedal 1989, 45, 503-528.
J. L. Faulon, M. Misra, S. Martin, K. Sale, R. Sapra, Bioinformatics 2008, 24, 225-233.
L. Jacob, J. P. Vert, Bioinformatics 2008, 24, 2149-2156.
Y. Tabei, Y. Yamanishi, BMC Syst. Biol. 2013, 7, S3.
G. Jacobson, PhD thesis Carnegie Mellon University, 1989.
P. Li, A. C. Köning, Proc. 27th Int. World Wide Web 2010, 671-680.
C. Shen, Y. Ding, J. Tang, X. Xu, F. Guo, Int. J. Mol. Sci. 2017, 18, 1781.
L. Yang, Y. A. Huang, Z. H. You, L. P. Li, Z. Wang, Molecules 2019, 24, 2999.
F. R. Meng, Z. H. You, X. Chen, Y. Zhou, J. Y. An, Molecules 2017, 22, 1119.
T. Cheng, M. Hao, T. Takeda, S. H. Bryant, Y. Wang, The AAPS Journal 2017, 19, 1264-1275.