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<p>The complexity of disease-causing signaling networks is indicative of the failure of single-target therapeutics to work, particularly because of feedback, redundancy and activation of compensatory responses. The review describes the recent movement to network pharmacology and purposeful polypharmacology facilitated by the emergence of artificial intelligence (AI) and massive biological knowledge graphs. This review explains how machine learning and graph neural networks can be used to characterize molecular interactions systematically, predict targets that are of disease relevance, as well as priorities on multi-target intervention strategies. Generative models and reinforcement-based learning strategies are addressed to create compounds and combinations of drugs designed to modulate networks, and not individual protein inhibition. It describes the experimental validation processes, such as CETSA, NanoBRET, and Perturb-seq, and patient-derived models and MIDD systems to aid the translational evidence. Data quality, bias, interpretability, and reproducibility are taken into consideration. In sum, this review presents a feasible and combined model of AI-assisted network-mediated drug discovery.</p><p></p>

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AI and network biology for rational polypharmacology in signaling drug design: a review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月5日

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  • Xuehao Li,
  • Zhaoqi Wu,
  • Te Fang,
  • Jun Li,
  • Danyang Li,
  • Ximeng Zhang,
  • Ling Liu,
  • Liming Wang

摘要

The complexity of disease-causing signaling networks is indicative of the failure of single-target therapeutics to work, particularly because of feedback, redundancy and activation of compensatory responses. The review describes the recent movement to network pharmacology and purposeful polypharmacology facilitated by the emergence of artificial intelligence (AI) and massive biological knowledge graphs. This review explains how machine learning and graph neural networks can be used to characterize molecular interactions systematically, predict targets that are of disease relevance, as well as priorities on multi-target intervention strategies. Generative models and reinforcement-based learning strategies are addressed to create compounds and combinations of drugs designed to modulate networks, and not individual protein inhibition. It describes the experimental validation processes, such as CETSA, NanoBRET, and Perturb-seq, and patient-derived models and MIDD systems to aid the translational evidence. Data quality, bias, interpretability, and reproducibility are taken into consideration. In sum, this review presents a feasible and combined model of AI-assisted network-mediated drug discovery.