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DL-SMILES#: A Novel Encoding Scheme for Predicting Compound Protein Affinity Using Deep Learning

Author(s):

Shudong Wang, Jiali Liu*, Mao Ding*, Yijun Gao*, Dayan Liu, Qingyu Tian and Jinfu Zhu   Pages 1 - 9 ( 9 )

Abstract:


Introduction: Drug repositioning aims to screen drugs and therapeutic goals from approved drugs and abandoned compounds that have been identified as safe. This trend is changing the landscape of drug development and creating a model of drug repositioning for new drug development. In the recent decade, machine learning methods have been applied to predict the binding affinity of compound proteins, while deep learning is recently becoming prominent and achieving significant performances. Among the models, the way of representing the compounds is usually simple, which is the molecular fingerprints, i.e., a single SMILES string.

Methods: In this study, we improve previous work by proposing a novel representing manner, named SMILES#, to recode the SMILES string. This approach takes into account the properties of compounds and achieves superior performance. After that, we propose a deep learning model that combines recurrent neural networks with a convolutional neural network with an attention mechanism, using unlabeled data and labeled data to jointly encode molecules and predict binding affinity.

Results: Experimental results show that SMILES# with compound properties can effectively improve the accuracy of the model and reduce the RMS error on most data sets.

Conclusion: We used the method to verify the related and unrelated compounds with the same target, and the experimental results show the effectiveness of the method.

Keywords:

Deep learning, drug repositioning, drug-target interactions, IC50 value, SMILES string, compound properties.

Affiliation:

College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, Shandong, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, Shandong, Department of Neurology Medicine, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250033, Department of Physiology, Shandong Provincial Key Laboratory of Pathogenesis and Prevention of Neurological Disorders and State Key Disciplines: Physiology, School of Basic Medicine, Qingdao University, Qingdao, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, Shandong, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, Shandong, School of Economics, Beijing Technology and Business University, Beijing, 100048



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