Scholarlink

<p>Predicting how small molecules affect diverse cell types phenotypically is central to drug discovery, yet it remains a challenging task. Modelling cell-type-specific transcriptional responses provides a scalable alternative for early candidate identification, enabling broader exploration and lower costs than exhaustive experimental exploration of the chemical space. Here we present PrePR-CT, a graph-based deep learning approach that utilizes cell-type-specific co-expression networks as an inductive bias to predict transcriptional responses to chemical perturbations. Graph attention networks learn biologically meaningful representations that capture cell-type-specific gene interactions, enabling gene-level attributions. Across five single-cell RNA sequencing datasets, including human blood and multiple cancer lines, one bulk transcriptomics dataset and a large-scale small-molecule screen, the method generalizes to unseen perturbations and previously unseen cell types under data-limited settings, achieving higher accuracy for expression variability compared to generative baselines. Attribution analyses identify high-attention genes that complement traditional differential expression analyses, highlighting pathway-specific mechanisms of small-molecule response. By combining scalability, robustness to distribution shifts and interpretability, PrePR-CT enables cell-type-resolved prediction of drug responses, providing a foundation for more precise modelling of cellular perturbations in early drug discovery.</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

Predicting and interpreting cell-type-specific drug responses in the small-data regime using inductive priors

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月16日

查看全文
  • Reem Alsulami,
  • Robert Lehmann,
  • Sumeer A. Khan,
  • Vincenzo Lagani,
  • Alberto Maillo,
  • David Gomez-Cabrero,
  • Narsis A. Kiani,
  • Jesper Tegner

摘要

Predicting how small molecules affect diverse cell types phenotypically is central to drug discovery, yet it remains a challenging task. Modelling cell-type-specific transcriptional responses provides a scalable alternative for early candidate identification, enabling broader exploration and lower costs than exhaustive experimental exploration of the chemical space. Here we present PrePR-CT, a graph-based deep learning approach that utilizes cell-type-specific co-expression networks as an inductive bias to predict transcriptional responses to chemical perturbations. Graph attention networks learn biologically meaningful representations that capture cell-type-specific gene interactions, enabling gene-level attributions. Across five single-cell RNA sequencing datasets, including human blood and multiple cancer lines, one bulk transcriptomics dataset and a large-scale small-molecule screen, the method generalizes to unseen perturbations and previously unseen cell types under data-limited settings, achieving higher accuracy for expression variability compared to generative baselines. Attribution analyses identify high-attention genes that complement traditional differential expression analyses, highlighting pathway-specific mechanisms of small-molecule response. By combining scalability, robustness to distribution shifts and interpretability, PrePR-CT enables cell-type-resolved prediction of drug responses, providing a foundation for more precise modelling of cellular perturbations in early drug discovery.