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Predicting synergistic effects between compounds through their structural similarity and effects on transcriptomes

机译:通过结构相似性和对转录组的影响预测化合物之间的协同作用

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Motivation: 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.
机译:动机:组合疗法一直在癌症治疗的深入研究中。但是,由于候选化合物之间可能存在大量的组合,因此详尽的筛选是令人望而却步的。因此,重要的是要开发能够预测化合物联合作用,确定组合优先次序并限制搜索空间以促进和加速组合疗法发展的计算工具。

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