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 cluster (possibly intersecting) detected by community detection algorithms and takes the ensemble labels for a compound as final prediction. Experimental evaluation based on the jackknife test on the benchmark dataset demonstrated that our method has boosted the absolute true rate, which is the most stringent evaluation metrics in this study, from 0.6330 to 0.7497, in comparison to the state-of-the-art approaches. Moreover, the community structures of the label relation graph were detected through the label propagation method. The advantage of multilabel learning over the single-label models was shown by label-wise analysis. Our study indicated that the proposed method ATC-NLSP, which adopts ideas from network research community and captures the correlation of labels in a data driven manner, is the top-performing model in the ATC prediction task. We believed that the power of NLSP remains to be unleashed for the multilabel learning tasks in drug discovery. The source codes are freely available at .

STS-NLSP: A Network-Based Label Space Partition Method for Predicting the Specificity of Membrane Transporter Substrates Using a Hybrid Feature of Structural and Semantic Similarity

“…The NLSP is a newly proposed multi-label learning method and has achieved top performance in many predictive tasks (Szymanski et al, 2016). This method has also recently reached the top performance in the drug classification and enzyme-substrate selectivity prediction tasks by our group (Shan et al, 2019; Wang et al, 2019). Inspired by these current advances, we adopted the data-driven NLSP method for the prediction of specificity of membrane transporter substrates.…”

Prediction of diabetic protein markers based on an ensemble method

“…The ensemble method uses many kinds of methods to process a dataset, which may obtain superior results [7,[70][71][72][73][74][75][76][77]. Different methods have different emphases on data processing.…”

T4SE-XGB: Interpretable Sequence-Based Prediction of Type IV Secreted Effectors Using eXtreme Gradient Boosting Algorithm

“…Extreme gradient boosting also named XGBoost (Chen and Guestrin, 2016 ) is an optimized distributed gradient boosting algorithm designed to be highly efficient, flexible, and portable (Wang et al, 2019a ). XGBoost based on decision tree ensembles consists of a set of classification or regression trees.…”