Combinatorial prediction of therapeutic targets using a causally-inspired neural network

Dataset supporting "Combinatorial prediction of therapeutic targets using a causally-inspired neural network"<b>Abstract: </b>Phenotype-driven drug discovery, as an emerging alternative to target-driven strategies, focuses on identifying compounds that counteract the overall effects of diseases by analyzing phenotypic signatures. Our study introduces a novel approach to this field, aiming to expand the search space for discovering new therapeutic agents. We introduce PDGrapher, a causally-inspired graph neural network model designed to predict arbitrary perturbagens – a set of therapeutic targets – capable of reversing disease effects. Unlike current methods, which are limited by their reliance on predefined compound libraries, PDGrapher employs a novel combinatorial prediction framework to widen the search scope. PDGrapher has demonstrated significant improvements in predicting effective perturbagens, as evidenced by our evaluation across four datasets of genetic and chemical interventions. Notably, PDGrapher successfully predicted effective perturbagens in up to 10% additional test samples and ranked known therapeutic targets up to 35% higher than competing methods. A key innovation of PDGrapher is its direct prediction capability, which contrasts with the indirect, computationally-intensive models traditionally used in phenotype-driven drug discovery. This direct approach enables PDGrapher to train up to 30 times faster, representing a significant leap in efficiency and effectiveness. The results from our study suggest that PDGrapher could substantially advance phenotype-driven drug discovery, offering a faster, more expansive approach to identifying novel therapeutic compounds.

支撑《基于因果启发神经网络的治疗靶点组合预测》研究的数据集
摘要：表型驱动型药物发现作为靶点驱动策略的新兴替代方案，核心目标是通过分析表型特征，筛选可抵消疾病整体病理效应的化合物。本研究针对该领域提出全新方法，旨在拓展新型治疗剂的搜索空间。我们提出PDGrapher——一款受因果启发的图神经网络（Graph Neural Network）模型，用于预测可逆转疾病效应的任意扰动因子（即一组治疗靶点）。与当前依赖预设化合物库的局限性方法不同，PDGrapher采用全新的组合预测框架以拓展搜索范围。通过对四组遗传与化学干预数据集的评估验证，PDGrapher在有效扰动因子预测任务中展现出显著性能提升。值得注意的是，相较于同类竞争方法，PDGrapher可在最多额外10%的测试样本中成功预测有效扰动因子，且将已知治疗靶点的排名提升最高达35%。PDGrapher的核心创新在于其直接预测能力，这与表型驱动型药物发现中传统采用的间接、计算密集型模型形成鲜明对比。该直接预测方式使得PDGrapher的训练速度最高可达传统方法的30倍，在效率与性能上实现了大幅跃升。本研究结果表明，PDGrapher可极大推动表型驱动型药物发现领域的发展，为识别新型治疗化合物提供了更快、覆盖范围更广的解决方案。