2026年3月17日出版的《细胞》杂志发表了密歇根州立大学陈斌研究组的最新成果，他们开发出基于深度学习的新发现和设计逆转疾病相关转录表型的治疗方法。
该课题组研究人员提出了化学结构基因表达谱预测器（GPS），这是一个基于深度学习的药物发现平台，以转录组学特征为指导，筛选大型化合物文库并优化先导分子。该课题组首先开发了一个模型，该模型仅从化学结构中捕获转录组扰动特征，并将其部署到文库化合物中。课题组人员改进了评分方法，并采用树搜索方法进行优化。通过结合结构-基因-活性关系，研究人员从转录组学数据中揭示药物机制。小组评估了GPS在多种疾病中的应用，并在两个案例中进行了广泛的验证。在肝细胞癌中，该研究组发现了两个独特的化合物系列，具有良好的细胞选择性和体内疗效。在特发性肺纤维化中，研究人员通过逆转来自单细胞转录组学的多种不同细胞类型的基因表达，确定了一种重新利用的候选药物和一种新的抗纤维化化合物。
研究人员表示，识别逆转疾病相关转录组特征的药物已被广泛探索用于药物再利用，但其在新药物发现方面的潜力仍未得到充分探索。
附：英文原文
Title: Deep-learning-based de novo discovery and design of therapeutics that reverse disease-associated transcriptional phenotypes
Author: Jing Xing, Mingdian Tan, Dmitry Leshchiner, Mengying Sun, Mohamed Abdelgied, Li Huang, Shreya Paithankar, Katie Uhl, Rama Shankar, Erika Lisabeth, Bilal Aleiwi, Tara Jager, Cameron Lawson, Ruoqiao Chen, Matthew Giletto, Reda Girgis, Richard R. Neubig, Samuel So, Edmund Ellsworth, Xiaopeng Li, Mei-Sze Chua, Jiayu Zhou, Bin Chen
Issue&Volume: 2026-03-17
Abstract: Identifying drugs that reverse disease-associated transcriptomic features has been widely explored for drug repurposing, but its potential for de novo drug discovery remains underexplored. Here, we present gene expression profile predictor on chemical structures (GPS), a deep-learning-based drug discovery platform, guided by transcriptomic features, that screens large compound libraries and optimizes lead molecules. We first develop a model that captures transcriptomic perturbation signatures solely from chemical structures and deploy it to library compounds. We refine scoring methods and employ a tree-search method for optimization. By incorporating structure-gene-activity relationships, we uncover drug mechanisms from transcriptomic data. We evaluate GPS across multiple diseases and conduct extensive validation in two cases. In hepatocellular carcinoma, we discover two unique compound series with favorable cellular selectivity and in vivo efficacy. In idiopathic pulmonary fibrosis, we identify one repurposing candidate and one novel anti-fibrotic compound by reversing gene expression of multiple distinct cell types derived from single-cell transcriptomics.
DOI: 10.1016/j.cell.2026.02.016
Source: https://www.cell.com/cell/abstract/S0092-8674(26)00223-0