College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
State Key Laboratory of Livestock and Poultry Breeding, Guangdong Public Laboratory of Animal Breeding and Nutrition, Guangdong Provincial Key Laboratory of Animal Breeding and Nutrition, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China.
Biochim Biophys Acta Mol Basis Dis. 2020 Nov 1;1866(11):165910. doi: 10.1016/j.bbadis.2020.165910. Epub 2020 Aug 6.
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.
药物解剖治疗化学（ATC）分类系统是一种广泛使用和接受的药物分类系统。它由世界卫生组织（WHO）推荐和维护。该系统中的每种药物都被分配一个或多个 ATC 代码，表明它在五个级别中的每个级别所属的类别。给定一种化学/药物，正确识别其在该系统中的 ATC 代码有助于了解其治疗效果。已经提出了几种计算方法来识别任何药物的第一级 ATC 类别。它们大多数在这方面构建了多标签分类器。先前的一项研究提出了一种截然不同的方案，该方案包含两种网络方法，分别基于最短路径（SP）和随机游走重启（RWR）算法，用于推断每个一级类别中的新型化学物质/药物。然而，由于 SP 和 RWR 算法的局限性，上述两种方法仍有许多隐藏的化学物质/药物无法发现。本研究采用了另一种经典的网络算法，即拉普拉斯热扩散（LHD）算法，来构建一种新的计算方法，用于识别每个一级 ATC 类别的新型潜在化学物质/药物。该算法应用于包含大量化学相互作用信息的化学网络，以评估候选化学物质/药物与每个 ATC 类别的关联。随后进行了三种筛选测试，以衡量对一个 ATC 类别的特异性和关联性，从而为每个类别产生更可靠的潜在成员。识别出了一些先前方法无法发现的隐藏化学物质/药物，并对它们进行了广泛分析，以确认它们可以成为相应 ATC 类别的新型成员。
