基于编程算法的模糊多细胞基因表达集成应用

Chengcheng Yuan, Hua Li, Yuanxia Zhang, Daoqing Gong
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引用次数: 0

摘要

众所周知,药物开发需要很长的时间和成本。由于化合物的未知性质,制药研究人员需要重复数百或数千次实验才能获得相对准确的结果。人工智能算法的出现为制药研究人员提供了极大的便利。研究人员将具有人工智能的机器学习算法应用于药物发现和开发领域,并取得了丰硕的成果。针对药物开发过程中涉及到的化合物的理化性质问题,采用模糊多细胞基因表达编程(FMCGEP)算法,将化合物毒性数据和化合物活性数据的分类与回归应用相结合。
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Ensemble Application of Fuzzy Multicellular Gene Expression by Programming Algorithm
It is well known that drug development takes a long time as well as cost. Due to the unknown nature of compounds, pharmaceutical researchers need to repeat hundreds or thousands of experiments to obtain relatively accurate results. The advent of artificial intelligence algorithms has provided great convenience for pharmaceutical researchers. Researchers have applied machine learning algorithms with artificial intelligence to the field of drug discovery and development and have achieved fruitful results. For the problem of physicochemical properties of compounds involved in the drug development process, with the FMCGEP(fuzzy multicellular gene expression programming) algorithm, we integrate the classification and regression application of compound toxicity data and compound activity data.
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