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<p>Protein–protein interactions (PPIs) are essential therapeutic targets, yet their large and relatively flat interfaces hinder the development of small-molecule inhibitors. Traditional computational approaches rely heavily on existing chemical libraries or expert heuristics, restricting exploration of novel chemical space. To address these challenges, we present Hot2Mol, a generative deep learning framework for the de novo design of target‐specific and drug‐like PPI inhibitors. Hot2Mol captures crucial pharmacophoric features from hot‐spot residues, allowing precise targeting of PPI interfaces while eliminating the need for known bioactive ligands. The framework integrates three main components: a conditional transformer for pharmacophore-guided, property-constrained molecular generation; an E(n)-equivariant graph neural network to ensure accurate spatial alignment with PPI hot-spot pharmacophores; a variational autoencoder to sample novel and diverse molecular structures. Comprehensive assessments demonstrate that Hot2Mol outperforms state-of-the-art models in binding affinity, drug-likeness, synthetic accessibility, novelty, and uniqueness. Molecular dynamics simulations further confirm the strong binding stability of generated compounds. Case studies underscore Hot2Mol’s ability to design high-affinity and selective PPI inhibitors, highlighting its potential to accelerate rational PPI-targeted drug discovery.</p>

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Hot-Spot-Guided Generative Deep Learning for Drug-Like PPI Inhibitor Design

  • 期刊论文

  • 发表日期：

    2025年9月2日

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  • Heqi Sun,
  • Jiayi Li,
  • Yufang Zhang,
  • Shenggeng Lin,
  • Junwei Chen,
  • Hong Tan,
  • Ruixuan Wang,
  • Xueying Mao,
  • Jianwei Zhao,
  • Rongpei Li,
  • Dong-Qing Wei

摘要

Protein–protein interactions (PPIs) are essential therapeutic targets, yet their large and relatively flat interfaces hinder the development of small-molecule inhibitors. Traditional computational approaches rely heavily on existing chemical libraries or expert heuristics, restricting exploration of novel chemical space. To address these challenges, we present Hot2Mol, a generative deep learning framework for the de novo design of target‐specific and drug‐like PPI inhibitors. Hot2Mol captures crucial pharmacophoric features from hot‐spot residues, allowing precise targeting of PPI interfaces while eliminating the need for known bioactive ligands. The framework integrates three main components: a conditional transformer for pharmacophore-guided, property-constrained molecular generation; an E(n)-equivariant graph neural network to ensure accurate spatial alignment with PPI hot-spot pharmacophores; a variational autoencoder to sample novel and diverse molecular structures. Comprehensive assessments demonstrate that Hot2Mol outperforms state-of-the-art models in binding affinity, drug-likeness, synthetic accessibility, novelty, and uniqueness. Molecular dynamics simulations further confirm the strong binding stability of generated compounds. Case studies underscore Hot2Mol’s ability to design high-affinity and selective PPI inhibitors, highlighting its potential to accelerate rational PPI-targeted drug discovery.