ATC-NLSP: Prediction of the Classes of Anatomical Therapeutic Chemicals Using a Network-Based Label Space Partition Method.

ABSTRACT: Anatomical Therapeutic Chemical (ATC) classification system proposed by the World Health Organization is a widely accepted drug classification scheme in both academic and industrial realm. It is a multilabeling system which categorizes drugs into multiple classes according to their therapeutic, pharmacological, and chemical attributes. In this study, we adopted a data-driven network-based label space partition (NLSP) method for prediction of ATC classes of a given compound within the multilabel learning framework. The proposed method ATC-NLSP is trained on the similarity-based features such as chemical-chemical interaction and structural and fingerprint similarities of a compound to other compounds belonging to the different ATC categories. The NLSP method trains predictors for each label

SUBMITTER: Wang X 

PROVIDER: S-EPMC6739564 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature