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<p>Structure-based molecule generation represents a notable advancement in artificial intelligence-driven drug design. However, progress in this field is constrained by the scarcity of structural data on protein–ligand complexes. Here we propose a latent variable approach that bridges the gap between ligand-only data and protein–ligand complexes, enabling target-aware generative models to explore a broader chemical space, thereby enhancing the quality of molecular generation. Inspired by quantum molecular simulations, we introduce ECloudGen, a generative model that leverages electron clouds as meaningful latent variables. ECloudGen incorporates techniques such as latent diffusion models, Llama architectures and a contrastive learning task, which organizes the chemical space into a structured and highly interpretable latent representation. Benchmark studies demonstrate that ECloudGen outperforms state-of-the-art methods by generating more potent binders with superior physiochemical properties and by covering a broader chemical space. The incorporation of electron clouds as latent variables not only improves generative performance but also introduces model-level interpretability, as illustrated in our case studies.</p>

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ECloudGen: leveraging electron clouds as a latent variable to scale up structure-based molecular design

  • 期刊论文

  • 发表日期：

    2025年10月15日

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  • Odin Zhang,
  • Jieyu Jin,
  • Zhenxing Wu,
  • Jintu Zhang,
  • Po Yuan,
  • Yuntao Yu,
  • Haitao Lin,
  • Haiyang Zhong,
  • Xujun Zhang,
  • Chenqing Hua,
  • Weibo Zhao,
  • Zhengshuo Zhang,
  • Kejun Ying,
  • Yufei Huang,
  • Huifeng Zhao,
  • Yu Kang,
  • Peichen Pan,
  • Jike Wang,
  • Dong Guo,
  • Shuangjia Zheng,
  • Chang-Yu Hsieh,
  • Tingjun Hou

摘要

Structure-based molecule generation represents a notable advancement in artificial intelligence-driven drug design. However, progress in this field is constrained by the scarcity of structural data on protein–ligand complexes. Here we propose a latent variable approach that bridges the gap between ligand-only data and protein–ligand complexes, enabling target-aware generative models to explore a broader chemical space, thereby enhancing the quality of molecular generation. Inspired by quantum molecular simulations, we introduce ECloudGen, a generative model that leverages electron clouds as meaningful latent variables. ECloudGen incorporates techniques such as latent diffusion models, Llama architectures and a contrastive learning task, which organizes the chemical space into a structured and highly interpretable latent representation. Benchmark studies demonstrate that ECloudGen outperforms state-of-the-art methods by generating more potent binders with superior physiochemical properties and by covering a broader chemical space. The incorporation of electron clouds as latent variables not only improves generative performance but also introduces model-level interpretability, as illustrated in our case studies.