GSFM: A genome-scale functional module transformation to represent drug efficacy for in silico drug discovery  

作　　者：Saisai Tian Xuyang Liao Wen Cao Xinyi Wu Zexi Chen Jinyuan Lu Qun Wang Jinbo Zhang Luonan Chen Weidong Zhang 

机构地区：[1]Department of Phytochemistry,School of Pharmacy,Second Military Medical University,Shanghai 200433,China [2]State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs,Institute of Medicinal Plant Development,Chinese Academy of Medical Sciences and Peking Union Medical College,Beijing 100193,China [3]Pharmacy College,Fujian University of Traditional Chinese Medicine,Fuzhou 350122,China [4]School of Pharmacy,Henan University,Kaifeng 475004,China [5]Key Laboratory of Systems Biology,Shanghai Institutes for Biological Sciences,Chinese Academy of Sciences,Shanghai 200031,China [6]School of Pharmacy,Anhui University of Chinese Medicine,Hefei 230012,China [7]Shanghai Frontiers Science Center of TCM Chemical Biology,Institute of Interdisciplinary Integrative Medicine Research,Shanghai University of Traditional Chinese Medicine,Shanghai 201203,China [8]Department of pharmacy,Tianjin Rehabilitation Center of Joint Logistics Support Force,Tianjin 300110,China [9]The Research Center for Traditional Chinese Medicine,Shanghai Institute of Infectious Diseases and Biosafety,Institute of Interdisciplinary Integrative Medicine Research,Shanghai University of Traditional Chinese Medicine,Shanghai 201203,China

出　　处：《Acta Pharmaceutica Sinica B》2025年第1期133-150,共18页药学学报(英文版)

基　　金：funded by the National Key Research and Development Program of China(2022YFC3502000);the National Natural Science Foundation of China(82141203,82274172,82430119);Shanghai Municipal Science and Technology Major Project(ZD2021CY001);Key project at central government level:The ability establishment of sustainable use for valuable Chinese medicine resources(2060302);Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine(ZYYCXTDD-202004);the support of Wild Goose Array Project,Zhengzhou Center of PLAJLSF,the Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission(23CGA45,Saisai Tian);Tianjin Health Research Project(TJWJ2024QN100).

摘　　要：Pharmacotranscriptomic profiles,which capture drug-induced changes in gene expression,offer vast potential for computational drug discovery and are widely used in modern medicine.However,current computational approaches neglected the associations within gene‒gene functional networks and unrevealed the systematic relationship between drug efficacy and the reversal effect.Here,we developed a new genome-scale functional module(GSFM)transformation framework to quantitatively evaluate drug efficacy for in silico drug discovery.GSFM employs four biologically interpretable quantifiers:GSFM_Up,GSFM_Down,GSFM_ssGSEA,and GSFM_TF to comprehensively evaluate the multidimension activities of each functional module(FM)at gene-level,pathway-level,and transcriptional regulatory network-level.Through a data transformation strategy,GSFM effectively converts noisy and potentially unreliable gene expression data into a more dependable FM active matrix,significantly outperforming other methods in terms of both robustness and accuracy.Besides,we found a positive correlation between RSGSFM and drug efficacy,suggesting that RSGSFM could serve as representative measure of drug efficacy.Furthermore,we identified WYE-354,perhexiline,and NTNCB as candidate therapeutic agents for the treatment of breast-invasive carcinoma,lung adenocarcinoma,and castration-resistant prostate cancer,respectively.The results from in vitro and in vivo experiments have validated that all identified compounds exhibit potent anti-tumor effects,providing proof-of-concept for our computational approach.

关 键 词：GSFM Data transformation strategy Multi-dimensions activities 

分 类 号：G63[文化科学—教育学]