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LDS-CNN: a deep learning framework for drug-target interactions prediction based on large-scale drug screening

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Abstract

Background

Drug-target interaction (DTI) is a vital drug design strategy that plays a significant role in many processes of complex diseases and cellular events. In the face of challenges such as extensive protein data and experimental costs, it is suggested to apply bioinformatics approaches to exploit potential interactions to design new targeted medications. Different data and interaction types bring difficulties to study involving incompatible and heterology formats. The analysis of drug-target interactions in a comprehensive and unified model is a significant challenge.

Method

Here, we propose a general method for predicting interactions between small-molecule drugs and protein targets, Large-scale Drug target Screening Convolutional Neural Network (LDS-CNN), which used unified encoding to achieve the calculation of the different data formats in an integrated model to realize feature abstraction and potential object prediction.

Result

On 898,412 interaction data involving 1683 small-molecule compounds and 14,350 human proteins from 8.8 billion records, the proposed method achieved an area under the curve (AUC) of 0.96, an area under the precision-recall curve (AUPRC) of 0.95, and an accuracy of 90.13%. The experimental results illustrated that the proposed method attained high accuracy on the test set, indicating its high predictive ability in drug-target interaction prediction. LDS-CNN is effective for the prediction of large-scale datasets and datasets composed of data with different formats.

Conclusion

In this study, we propose a DTI prediction method to solve the problems of unified encoding of large-scale data in multiple formats. It provides a feasible way to efficiently abstract the features among different types of drug-related data, thus reducing experimental costs and time consumption. The proposed method can be used to identify potential drug targets and candidates for the treatment of complex diseases. This work provides a reference for DTI to process large-scale data and different formats with deep learning methods and provides certain suggestions for future research.

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Data availability

All datasets used in this study are listed in the article. The code and data of the method are freely available at: https://github.com/ZuxianZhang/LDS-CNN.

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Funding

This work was supported in part by the National Key R&D Program of China under Grant No. 2022YFB3304400, in part by the China University Industry, University and Research Innovation Fund under Grant No. 2021FNA03002, and in part by the Shanghai Pujiang Programme under Grant No. 22PJD104.

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Authors and Affiliations

  1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, China

    Yang Wang & Dongning Liu

  2. School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, 510006, China

    Zuxian Zhang, Ying Huang, Yihan Zhang & Yu-Jing Lu

  3. The First Affiliated Hospital of Ningbo University, Ningbo, 315010, China

    Chenghong Piao

  4. Smart Medical Innovation Technology Center, Guangdong University of Technology, Guangzhou, 510006, China

    Yu-Jing Lu

  5. Shanghai Institute of Biological Products, Shanghai, 201403, China

    Chi Zhang

Authors
  1. Yang Wang
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  4. Ying Huang
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Correspondence to Yu-Jing Lu or Dongning Liu.

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Wang, Y., Zhang, Z., Piao, C. et al. LDS-CNN: a deep learning framework for drug-target interactions prediction based on large-scale drug screening. Health Inf Sci Syst 11, 42 (2023). https://doi.org/10.1007/s13755-023-00243-w

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