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In previous years, most of the traditional molecular representations are based on hand-crafted features and rely heavily on biological experimentations, which are often costly and time consuming. However, recent researches achieve promising results using machine learning on various domains. In this article, we present a novel method named Smi2Vec-BiGRU that is designed for learning atoms and solving the single- and multitask binary classification problems in the field of drug discovery, which are the basic and also key problems in this field. Specifically, our approach transforms the molecule data in the SMILES format into a set of sample vectors and then feeds them into the bidirectional gated recurrent unit neural networks for training, which learns low-dimensional vector representations for molecular drug. We conduct extensive experiments on several widely used benchmarks including Tox21, SIDER and ClinTox. The experimental results show that our approach can achieve state-of-the-art performance on these benchmarking datasets, demonstrating the feasibility and competitiveness of our proposed approach.<\/jats:p>","DOI":"10.1093\/bib\/bbz125","type":"journal-article","created":{"date-parts":[[2019,9,11]],"date-time":"2019-09-11T19:25:36Z","timestamp":1568229936000},"page":"2099-2111","source":"Crossref","is-referenced-by-count":147,"title":["A novel molecular representation with BiGRU neural networks for learning atom"],"prefix":"10.1093","volume":"21","author":[{"given":"Xuan","family":"Lin","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Hunan University, Changsha, 410082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Quan","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Hunan University, Changsha, 410082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6865-7899","authenticated-orcid":false,"given":"Zhi-Jie","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Hunan University, Changsha, 410082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huang","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Computer, National University of Defense Technology, Changsha, 410073,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangxiang","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Hunan University, Changsha, 410082, China"},{"name":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 510275, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2019,11,15]]},"reference":[{"issue":"6","key":"2020120619044027600_ref1","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1016\/j.drudis.2018.01.039","article-title":"The rise of deep learning in drug discovery","volume":"23","author":"Chen","year":"2018","journal-title":"Drug Discov Today"},{"issue":"3","key":"2020120619044027600_ref2","doi-asserted-by":"crossref","first-page":"1336","DOI":"10.1109\/JBHI.2018.2852274","article-title":"Ensemble prediction of synergistic drug combinations incorporating biological, chemical, pharmacological, and network knowledge","volume":"23","author":"Ding","year":"2018","journal-title":"IEEE J Biomed Health Inform"},{"key":"2020120619044027600_ref3","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"issue":"6","key":"2020120619044027600_ref4","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1109\/TNNLS.2017.2654357","article-title":"A parallel multiclassification algorithm for big data using an extreme learning machine","volume":"29","author":"Duan","year":"2017","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"1","key":"2020120619044027600_ref5","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1109\/JBHI.2018.2814609","article-title":"Inferring microrna targets based on restricted Boltzmann machines","volume":"23","author":"Liu","year":"2018","journal-title":"IEEE J Biomed Health Inform"},{"key":"2020120619044027600_ref6","article-title":"Parallel protein community detection in large-scale ppi networks based on multi-source learning","author":"Chen","year":"2018","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2020120619044027600_ref7","article-title":"SW-tandem: a highly efficient tool for large-scale peptide sequencing with parallel spectrum dot product on sunway taihulight","author":"Li","year":"2019","journal-title":"Bioinformatics"},{"issue":"1","key":"2020120619044027600_ref8","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1021\/ci00057a005","article-title":"SMILES, a chemical language and information system. 1. 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