AIDrugDesigner: a web server for drug-like molecules generation and optimization

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ID: 329457
2026
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Abstract
MOTIVATION: Designing molecules with predefined properties remains a critical yet challenging task in drug discovery. Recent advances in deep learning have demonstrated strong potential in accelerating the identification of novel compounds with desired properties and activities. However, the implementation and running of these models often present difficulties due to varying environments and the requisite for additional coding competencies. RESULTS: Here, we present AIDrugDesigner, an integrated web server for practical deep learning-driven drug design. The platform consists of two modules: generation and optimization. The generation module supports target-aware drug design, pharmacophore-guided, property-conditioned, and linker design from diverse inputs. The optimization module refines user-provided molecules with respect to drug-likeness, LogP, and synthetic accessibility. By integrating multiple generative strategies into a unified interface, AIDrugDesigner reduces the technical barrier for applying deep learning models in drug discovery. Systematic evaluations across multiple design tasks further demonstrate its versatility. AVAILABILITY AND IMPLEMENTATION: The server is freely available at https://www.csuligroup.com/AIDrugDesigner.
Reference Key
openalex_W7214057907 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Huimin Zhu, Renyi Zhou, Shiliang Zhang, Yifan Wu, Zhangli Lu, Dongsheng Cao, Min Li
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag693
URL
https://doi.org/10.1093/bioinformatics/btag693
Keywords Keywords not found

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