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Recognizing novel chemicals/drugs for anatomical therapeutic chemical classes with a heat diffusion algorithm

文献类型： 外文期刊

作者： Liang, Haiyan 1 ; Hu, Bin 2 ; Chen, Lei 1 ; Wang, Shuaiqun 1 ; Aorigele 3 ;

作者机构： 1.Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China

2.Guangdong Acad Agr Sci, Inst Anim Sci, State Key Lab Livestock & Poultry Breeding, Guangdong Publ Lab Anim Breeding & Nutr,Guangdong, Guangzhou 510640, Peoples R China

3.Univ Toyama, Fac Engn, Toyama, Japan

关键词： Anatomical therapeutic chemical; Laplacian heat diffusion algorithm; Chemical interaction

期刊名称：BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR BASIS OF DISEASE （ 影响因子：5.187； 五年影响因子：6.244 ）

ISSN： 0925-4439

年卷期： 2020 年 1866 卷 11 期

页码：

收录情况： SCI

摘要： Drug Anatomical Therapeutic Chemical (ATC) classification system is a widely used and accepted drug classification system. It is recommended and maintained by World Health Organization (WHO). Each drug in this system is assigned one or more ATC codes, indicating which classes it belongs to in each of five levels. Given a chemical/drug, correct identification of its ATC codes in such system can be helpful to understand its therapeutic effects. Several computational methods have been proposed to identify the first level ATC classes for any drug. Most of them built multi-label classifiers in this regard. One previous study proposed a quite different scheme, which contained two network methods, based on shortest path (SP) and random walk with restart (RWR) algorithms, respectively, to infer novel chemicals/drugs for each first level class. However, due to the limitations of SP and RWR algorithms, there still exist lots of hidden chemicals/drugs that above two methods cannot discover. This study employed another classic network algorithm, Laplacian heat diffusion (LHD) algorithm, to construct a new computational method for recognizing novel latent chemicals/drugs of each first level ATC class. This algorithm was applied on a chemical network, which containing lots of chemical interaction information, to evaluate the associations of candidate chemicals/drugs and each ATC class. Three screening tests, which measured the specificity and association to one ATC class, followed to yield more reliable potential members for each class. Some hidden chemicals/drugs were recognized, which cannot be found out by previous methods, and they were extensively analyzed to confirm that they can be novel members in the corresponding ATC class.

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