{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,5,7]],"date-time":"2023-05-07T04:23:24Z","timestamp":1683433404875},"reference-count":30,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2023,5,1]]},"DOI":"10.1587\/transinf.2022dlp0023","type":"journal-article","created":{"date-parts":[[2023,4,30]],"date-time":"2023-04-30T22:24:00Z","timestamp":1682893440000},"page":"697-706","source":"Crossref","is-referenced-by-count":0,"title":["MolHF: Molecular Heterogeneous Attributes Fusion for Drug-Target Affinity Prediction on Heterogeneity"],"prefix":"10.1587","volume":"E106.D","author":[{"given":"Runze","family":"WANG","sequence":"first","affiliation":[{"name":"Taiyuan University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zehua","family":"ZHANG","sequence":"additional","affiliation":[{"name":"Taiyuan University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yueqin","family":"ZHANG","sequence":"additional","affiliation":[{"name":"Taiyuan University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongyuan","family":"JIANG","sequence":"additional","affiliation":[{"name":"Xidian University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shilin","family":"SUN","sequence":"additional","affiliation":[{"name":"Taiyuan University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guixiang","family":"MA","sequence":"additional","affiliation":[{"name":"University of Illinois at Chicago"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] S. Lu, Q. Ye, D. Singh, Y. Cao, J.K. Diedrich, J.R. Yates, E. Villa, D.W. Cleveland, and K.D. Corbett, \u201cThe SARS-CoV-2 nucleocapsid phosphoprotein forms mutually exclusive condensates with RNA and the membrane-associated M protein,\u201d Nature communications, vol.12, pp.1-15, Jan. 2021. 10.1038\/s41467-020-20768-y","DOI":"10.1038\/s41467-020-20768-y"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] S.S. Abdool Karim, T. de Oliveira, \u201cNew SARS-CoV-2 variants \u2014 clinical, public health, and vaccine implications,\u201d New England Journal of Medicine, vol.384, pp.1866-1868, May 2021. 10.1056\/nejmc2100362","DOI":"10.1056\/NEJMc2100362"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] M. Bagherian, E. Sabeti, K. Wang, M.A. Sartor, Z. Nikolovska-Coleska, and K. Najarian, \u201cMachine learning approaches and databases for prediction of drug-target interaction: a survey paper,\u201d Briefings in bioinformatics, vol.22, no.1, pp.247-269, Jan. 2021. 10.1093\/bib\/bbz157","DOI":"10.1093\/bib\/bbz157"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] H.C.S. Chan, H. Shan, T. Dahoun, H. Vogel, and S. Yuan, \u201cAdvancing drug discovery via artificial intelligence,\u201d Trends in pharmacological sciences, vol.40, pp.801-801, Oct. 2019. 10.1016\/j.tips.2019.07.013","DOI":"10.1016\/j.tips.2019.07.013"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] L. Peska, K. Buza, and J. Koller, \u201cDrug-target interaction prediction: A Bayesian ranking approach,\u201d Computer methods and programs in biomedicine, vol.152, pp.15-21, Dec. 2017. 10.1016\/j.cmpb.2017.09.003","DOI":"10.1016\/j.cmpb.2017.09.003"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] O. Trott and A.J. Olson, \u201cAutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading,\u201d Journal of computational chemistry, vol.31, no.2, pp.455-461, Jan. 2010. 10.1002\/jcc.21334","DOI":"10.1002\/jcc.21334"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] L. Perlman, A. Gottlieb, N. Atias, E. Ruppin, and R. Sharan, \u201cCombining drug and gene similarity measures for drug-target elucidation,\u201d Journal of computational biology, vol.18, no.2, pp.133-145, Feb. 2011. 10.1089\/cmb.2010.0213","DOI":"10.1089\/cmb.2010.0213"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] M. Wang, C. Tang, and J. Chen, \u201cDrug-target interaction prediction via dual Laplacian graph regularized matrix completion,\u201d BioMed Research International, vol.2018, 2018. 10.1155\/2018\/1425608","DOI":"10.1155\/2018\/1425608"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] T. He, M. Heidemeyer, F. Ban, A. Cherkasov, and M. Ester, \u201cSimBoost: a read-across approach for predicting drug-target binding affinities using gradient boosting machines,\u201d Journal of cheminformatics, vol.9, pp.1-14, April 2017. 10.1186\/s13321-017-0209-z","DOI":"10.1186\/s13321-017-0209-z"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] W. Zhang, Y. Chen, and D. Li, \u201cDrug-target interaction prediction through label propagation with linear neighborhood information,\u201d Molecules, vol.22, no.12, p.2056, Dec. 2017. 10.3390\/molecules22122056","DOI":"10.3390\/molecules22122056"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] Y. Liu, M. Wu, C. Miao, P. Zhao, X.-L. Li, and T.M. Przytycka, \u201cNeighborhood regularized logistic matrix factorization for drug-target interaction prediction,\u201d PLoS computational biology, vol.12, no.2, Feb. 2016. 10.1371\/journal.pcbi.1004760","DOI":"10.1371\/journal.pcbi.1004760"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] Y. Ding, J. Tang, and F. Guo, \u201cIdentification of drug-target interactions via multiple information integration,\u201d Information Sciences, vol.418-419, pp.546-560, Dec. 2017. 10.1016\/j.ins.2017.08.045","DOI":"10.1016\/j.ins.2017.08.045"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] S. Matsumoto, S. Ishida, M. Araki, T. Kato, K. Terayama, and Y. Okuno, \u201cExtraction of protein dynamics information from cryo-EM maps using deep learning,\u201d Nature Machine Intelligence, vol.3, pp.153-160, Feb. 2021. 10.1038\/s42256-020-00290-y","DOI":"10.1038\/s42256-020-00290-y"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] E. Callaway, \u201c\u2018It will change everything\u2019: DeepMind&apos;s AI makes gigantic leap in solving protein structures,\u201d Nature, vol.588, pp.203-204, Dec. 2020. 10.1038\/d41586-020-03348-4","DOI":"10.1038\/d41586-020-03348-4"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] M. Sun, S. Zhao, C. Gilvary, O. Elemento, J. Zhou and F. Wang, \u201cGraph convolutional networks for computational drug development and discovery,\u201d Briefings in bioinformatics, vol.21, no.3, pp.919-935, May 2020. 10.1093\/bib\/bbz042","DOI":"10.1093\/bib\/bbz042"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] K. Huang, C. Xiao, L.M. Glass, and J. Sun, \u201cMolTrans: Molecular Interaction Transformer for drug-target interaction prediction,\u201d Bioinformatics, vol.37, no.6, pp.830-836, March 2021. 10.1093\/bioinformatics\/btaa880","DOI":"10.1093\/bioinformatics\/btaa880"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] H. \u00f6zt\u00fcrk, A. zg\u00fcr, and E. Ozkirimli, \u201cDeepDTA: Deep Drug-Target Binding Affinity Prediction,\u201d Bioinformatics, vol.34, no.17, pp.821-829, Sept. 2018. 10.1093\/bioinformatics\/bty593","DOI":"10.1093\/bioinformatics\/bty593"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] M. Karimi, D. Wu, Z. Wang, and Y. Shen, \u201cDeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks,\u201d Bioinformatics, vol.35, pp.3329-3338, Sept. 2019. 10.1101\/351601","DOI":"10.1093\/bioinformatics\/btz111"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] L. Zhao, J. Wang, L. Pang, Y. Liu, and J. Zhang, \u201cGANsDTA: Predicting Drug-Target Binding Affinity Using GANs,\u201d Frontiers in Genetics, vol.10, Jan. 2020. 10.3389\/fgene.2019.01243","DOI":"10.3389\/fgene.2019.01243"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] K.Y. Gao, A. Fokoue, H. Luo, A. Iyengar, S. Dey, and P. Zhang, \u201cInterpretable Drug Target Prediction Using Deep Neural Representation,\u201d Twenty-Seventh International Joint Conference on Artificial Intelligence IJCAI-18, pp.3371-3377, 2018. 10.24963\/ijcai.2018\/468","DOI":"10.24963\/ijcai.2018\/468"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[21] M. Tsubaki, K. Tomii, and J. Sese, \u201cCompound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences,\u201d Bioinformatics, vol.35, no.2, pp.309-318, Jan. 2019. 10.1093\/bioinformatics\/bty535","DOI":"10.1093\/bioinformatics\/bty535"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] T. Nguyen, H. Le, T.P. Quinn, T. Nguyen, T.D. Le, and S. Venkatesh, \u201cGraphDTA: Predicting drug-target binding affinity with graph neural networks,\u201d Bioinformatics, vol.37, no.8, pp.1140-1147, May 2021. 10.1093\/bioinformatics\/btaa921","DOI":"10.1093\/bioinformatics\/btaa921"},{"key":"23","unstructured":"[23] X. Lin, K. Zhao, and T. Xiao, \u201cDeepGS: Deep representation learning of graphs and sequences for drug-target binding affinity prediction,\u201d 24th European Conference on Artificial Intelligence (ECAI), 2020."},{"key":"24","unstructured":"[24] F.T.N, Kip and M. Welling, \u201cSemi-supervised classification with graph convolutional networks,\u201d 5th International Conference on Learning Representations (ICLR), 2017."},{"key":"25","unstructured":"[25] P. Velikovi, G. Cucurull, and A. Casanova, \u201cGraph attention networks,\u201d 6th International Conference on Learning Representations (ICLR), 2018."},{"key":"26","unstructured":"[26] K. Xu, W. Hu, and J. Leskovec, \u201cHow powerful are graph neural networks?,\u201d 7th International Conference on Learning Representations (ICLR), 2019."},{"key":"27","unstructured":"[27] K. Ishiguro, S. Maeda, and M. Koyama, \u201cGraph warp module: An auxiliary module for boosting the power of graph neural networks in molecular graph analysis,\u201d arXiv, vol.abs\/1902.01020, 2019."},{"key":"28","doi-asserted-by":"publisher","unstructured":"[28] L. Wei, X. Ye, Y. Xue, T. Sakurai, and L. Wei, \u201cATSE: a peptide toxicity predictor by exploiting structural and evolutionary information based on graph neural network and attention mechanism,\u201d Briefings in Bioinformatics, vol.22, no.5, April 2021. 10.1093\/bib\/bbab041","DOI":"10.1093\/bib\/bbab041"},{"key":"29","doi-asserted-by":"publisher","unstructured":"[29] M.I. Davis, J.P. Hunt, and S. Herrgard, \u201cComprehensive analysis of kinase inhibitor selectivity,\u201d Nature Biotechnology, vol.29, pp.1046-1051, Nov. 2011. 10.1038\/nbt.1990","DOI":"10.1038\/nbt.1990"},{"key":"30","doi-asserted-by":"publisher","unstructured":"[30] J. Tang, A. Szwajda, S. Shakyawar, T. Xu, P. Hintsanen, K. Wennerberg, and T. Aittokallio, \u201cMaking Sense of Large-Scale Kinase Inhibitor Bioactivity Data Sets: A Comparative and Integrative Analysis,\u201d Journal of Chemical Information and Modeling, vol.54, no.3, pp.735-743, March 2014. 10.1021\/ci400709d","DOI":"10.1021\/ci400709d"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E106.D\/5\/E106.D_2022DLP0023\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,6]],"date-time":"2023-05-06T04:16:48Z","timestamp":1683346608000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E106.D\/5\/E106.D_2022DLP0023\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,1]]},"references-count":30,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2022dlp0023","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,1]]},"article-number":"2022DLP0023"}}