Scholarlink

<p>While drug combinations are increasingly important in disease treatment, predicting their therapeutic interactions remains challenging. This review systematically analyzes computational methods for predicting drug combination effects through multi-omics data integration. We comprehensively assess key algorithms including DrugComboRanker and AuDNNsynergy, and evaluate integration approaches encompassing kernel regression and graph networks. The review elucidates artificial intelligence applications in predicting drug synergistic and antagonistic effects.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Computational frameworks transform antagonism to synergy in optimizing combination therapies

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月19日

查看全文
  • Jinghong Chen,
  • Anqi Lin,
  • Aimin Jiang,
  • Chang Qi,
  • Zaoqu Liu,
  • Quan Cheng,
  • Shuofeng Yuan,
  • Peng Luo

摘要

While drug combinations are increasingly important in disease treatment, predicting their therapeutic interactions remains challenging. This review systematically analyzes computational methods for predicting drug combination effects through multi-omics data integration. We comprehensively assess key algorithms including DrugComboRanker and AuDNNsynergy, and evaluate integration approaches encompassing kernel regression and graph networks. The review elucidates artificial intelligence applications in predicting drug synergistic and antagonistic effects.