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Multi-scale Feature Fusion Neural Network for Accurate Prediction of Drug-Target Interactions

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Neural Information Processing (ICONIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1964))

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Abstract

Identification of drug-target interactions (DTI) is crucial in drug discovery and repositioning. However, identifying DTI is a costly and time-consuming process that involves conducting biological experiments with a vast array of potential compounds. To accelerate this process, computational methods have been developed, and with the growth of available datasets, deep learning methods have been widely applied in this field. Despite the emergence of numerous sequence-based deep learning models for DTI prediction, several limitations endure. These encompass inadequate feature extraction from protein targets using amino acid sequences, a deficiency in effective fusion mechanisms for drug and target features, and a prevalent inclination among many methods to solely treat DTI as a binary classification problem, thereby overlooking the crucial aspect of predicting binding affinity that signifies the strength of drug-target interactions. To address these concerns, we developed a multi-scale feature fusion neural network (MSF-DTI), which leverages the potential semantic information of amino acid sequences at multiple scales, enriches the feature representation of proteins, and fuses drug and target features using a designed feature fusion module for predicting drug-target interactions. According to experimental results, MSF-DTI outperforms other state-of-the-art methods in both DTI classification and binding affinity prediction tasks.

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Authors and Affiliations

  1. School of Computing Science, Sichuan Normal University, Chengdu, 610066, China

    Zhibo Yang, Binhao Bai, Jinyu Long & Junli Li

  2. Visual Computing and Virtual Reality Key Laboratory of Sichuan, Sichuan Normal University, Chengdu, 610068, China

    Zhibo Yang, Binhao Bai, Jinyu Long & Junli Li

  3. Sichuan Academy of Traditional Chinese Medicine/Sichuan Center of Translational Medicine/Sichuan Key Laboratory of Translational Medicine of Traditional Chinese Medicine, Chengdu, 610041, China

    Ping Wei

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  1. Zhibo Yang
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  2. Binhao Bai
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  3. Jinyu Long
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  4. Ping Wei
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  5. Junli Li
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Corresponding author

Correspondence to Junli Li .

Editor information

Editors and Affiliations

  1. School of Automation, Central South University, Changsha, China

    Biao Luo

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. School of Automation, Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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Yang, Z., Bai, B., Long, J., Wei, P., Li, J. (2024). Multi-scale Feature Fusion Neural Network for Accurate Prediction of Drug-Target Interactions. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1964. Springer, Singapore. https://doi.org/10.1007/978-981-99-8141-0_14

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  • DOI: https://doi.org/10.1007/978-981-99-8141-0_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8140-3

  • Online ISBN: 978-981-99-8141-0

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