title: Predicting synergistic effects between compounds through their structural similarity and effects on transcriptomes.
abstract: Combinatorial therapies have been under intensive research for cancer treatment. However, due to the large number of possible combinations among candidate compounds, exhaustive screening is prohibitive. Hence, it is important to develop computational tools that can predict compound combination effects, prioritize combinations and limit the search space to facilitate and accelerate the development of combinatorial therapies.
  
  
  
  In this manuscript we consider the NCI-DREAM Drug Synergy Prediction Challenge dataset to identify features informative about combination effects. Through systematic exploration of differential expression profiles after single compound treatments and comparison of molecular structures of compounds, we found that synergistic levels of combinations are statistically significantly associated with compounds' dissimilarity in structure and similarity in induced gene expression changes. These two types of features offer complementary information in predicting experimentally measured combination effects of compound pairs. Our findings offer insights on the mechanisms underlying different combination effects and may help prioritize promising combinations in the very large search space.
  
  
  
  The R code for the analysis is available on https://github.com/YiyiLiu1/DrugCombination CONTACT: hongyu.zhao@yale.eduSupplementary information: Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Biostatistics, School of Public Health, Yale University New Haven, CT, 06520, USA.
authors.forename: Yiyi
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Department of Biostatistics, School of Public Health, Yale University New Haven, CT, 06520, USA.|Program of Computational Biology and Bioinformatics, CT0610, Yale University, New Haven, CT, 06511, USA.
authors.forename: Hongyu
authors.initials: H
authors.lastname: Zhao
mesh_terms: D019295:Computational Biology / Q000379:methods*; D004357:Drug Synergism; D015870:Gene Expression; D006801:Humans; D016015:Logistic Models; D015394:Molecular Structure; D009369:Neoplasms / Q000188:drug therapy / Q000235:genetics; D059467:Transcriptome