A novel molecular representation with BiGRU neural networks for learning atom. (15th November 2019)

• Record Type: Journal Article
• Authors: Lin, Xuan; Quan, Zhe; Wang, Zhi-Jie; Huang, Huang; Zeng, Xiangxiang
• Is Part Of: Briefings in bioinformatics. Volume 21:Number 6(2020)
• Page Start: 2099
• Page End: 2111
• Publication Date: 2019-11-15
• Subjects: machine learning -- molecular representation -- recurrent neural networks -- drug discovery; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbz125
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Molecular representations play critical roles in researching drug design and properties, and effective methods are beneficial to assisting in the calculation of molecules and solving related problem in drug discovery. 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.