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<p>Generative drug design opens avenues for discovering novel compounds within the vast chemical space rather than conventional screening against limited libraries. However, the practical utility of the generated molecules is frequently constrained, as many designs prioritize a narrow range of pharmacological properties and neglect physical reliability, which hinders the success rate of subsequent wet-laboratory evaluations. Here, to address this, we propose ED2Mol, a deep learning-based approach that leverages fundamental electron density information to improve de novo molecular generation and optimization. The extensive evaluations across multiple benchmarks demonstrate that ED2Mol surpasses existing methods in terms of the generation success rate and &gt;97% physical reliability. It also facilitates automated hit optimization that is not fully implemented by other methods using fragment-based strategies. Furthermore, ED2Mol exhibits generalizability to more challenging, unseen allosteric pocket benchmarks, attaining consistent performance. More importantly, ED2Mol has been applied to various real-world essential targets, successfully identifying wet-laboratory-validated bioactive compounds, ranging from FGFR3 orthosteric inhibitors to CDC42 allosteric inhibitors, GCK and GPRC5A allosteric activators. The directly generated binding modes of these compounds are close to predictions through molecular docking and further validated via the X-ray co-crystal structure. All these results highlight ED2Mol’s potential as a useful tool in drug design with enhanced effectiveness, physical reliability and practical applicability.</p>

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Electron-density-informed effective and reliable de novo molecular design and optimization with ED2Mol

  • 期刊论文

  • 发表日期：

    2025年8月20日

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  • Mingyu Li,
  • Kun Song,
  • Jixiao He,
  • Mingzhu Zhao,
  • Gengshu You,
  • Jie Zhong,
  • Mengxi Zhao,
  • Arong Li,
  • Yu Chen,
  • Guobin Li,
  • Ying Kong,
  • Jiacheng Wei,
  • Zhaofu Wang,
  • Jiamin Zhou,
  • Hongbing Yang,
  • Shichao Ma,
  • Hailong Zhang,
  • Irakoze Loïca Mélita,
  • Weidong Lin,
  • Yuhang Lu,
  • Zhengtian Yu,
  • Xun Lu,
  • Yujun Zhao,
  • Jian Zhang

摘要

Generative drug design opens avenues for discovering novel compounds within the vast chemical space rather than conventional screening against limited libraries. However, the practical utility of the generated molecules is frequently constrained, as many designs prioritize a narrow range of pharmacological properties and neglect physical reliability, which hinders the success rate of subsequent wet-laboratory evaluations. Here, to address this, we propose ED2Mol, a deep learning-based approach that leverages fundamental electron density information to improve de novo molecular generation and optimization. The extensive evaluations across multiple benchmarks demonstrate that ED2Mol surpasses existing methods in terms of the generation success rate and >97% physical reliability. It also facilitates automated hit optimization that is not fully implemented by other methods using fragment-based strategies. Furthermore, ED2Mol exhibits generalizability to more challenging, unseen allosteric pocket benchmarks, attaining consistent performance. More importantly, ED2Mol has been applied to various real-world essential targets, successfully identifying wet-laboratory-validated bioactive compounds, ranging from FGFR3 orthosteric inhibitors to CDC42 allosteric inhibitors, GCK and GPRC5A allosteric activators. The directly generated binding modes of these compounds are close to predictions through molecular docking and further validated via the X-ray co-crystal structure. All these results highlight ED2Mol’s potential as a useful tool in drug design with enhanced effectiveness, physical reliability and practical applicability.