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  1. nature
  2. npj precision oncology
  3. review
  4. article
AI accelerate the identification of druggable targets by 3D structures of proteins and compounds
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  • Open access
  • Published: 14 February 2026

AI accelerate the identification of druggable targets by 3D structures of proteins and compounds

  • Da Li1 na1,
  • Sanbao Shi1 na1,
  • Zhiyu Yu1,
  • Peng Xu1 &
  • …
  • Cheng Zhang1 

npj Precision Oncology , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Cancer
  • Computational biology and bioinformatics
  • Drug discovery
  • Mathematics and computing

Abstract

Artificial intelligence (AI) is being used in oncological drug development to address the high costs, low success rates, and long timelines that characterize traditional drug development pipelines. The use of machine learning (ML) and deep learning (DL) models in computer-aided drug design is constantly growing owing to their capacity to analyze large, heterogeneous datasets, their ability to capture nonlinear biological trends, and their integration of various molecular and clinical characteristics. AI applications accelerate target discovery by predicting protein structures, ranking disease-relevant genes, and assessing target drugability. AI can be used to conduct rapid searches of multiplexed chemical libraries, predict drug-target interactions, and optimize the pharmacological and physicochemical properties of drugs in virtual screening. Advanced neural network designs also aid in de novo drug design, which involves developing new molecular structures with therapeutic properties of interest. This review outlines how AI has been used for target identification, virtual screening, de novo molecular design, and, specifically, in cancer applications. It further discusses the major issues in AI-based drug development, such as data quality, model interpretation, computational constraints, and ethical and regulatory considerations, which remain essential obstacles to broader clinical translation.

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Acknowledgements

This study was funded by the Liaoning Provincial Science and Technology Joint Program (Natural Science Foundation General Program, Grant No. 2024-MSLH-536).

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  1. Department of General Surgery, General Hospital of Northern Theater Command, Shenyang, Liaoning Province, China

    Da Li, Sanbao Shi, Zhiyu Yu, Peng Xu & Cheng Zhang

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Da Li. Sanbao Shi.: Conceptualization, methodology, writing—original draft; Writing— review. Zhiyu Yu. Peng Xu.: Data curation, Formal analysis; Writing–review and editing. Cheng Zhang.: Supervision, writing–review and editing. All authors reviewed and approved the final manuscript.

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Li, D., Shi, S., Yu, Z. et al. AI accelerate the identification of druggable targets by 3D structures of proteins and compounds. npj Precis. Onc. (2026). https://doi.org/10.1038/s41698-026-01310-7

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  • Received: 20 October 2025

  • Accepted: 25 January 2026

  • Published: 14 February 2026

  • DOI: https://doi.org/10.1038/s41698-026-01310-7

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