NerLTR-DTA: drug–target binding affinity prediction based on neighbor relationship and learning to rank

• Published in: Bioinformatics Vol. 38; no. 7; pp. 1964 - 1971
• Main Authors: Ru, Xiaoqing, Ye, Xiucai, Sakurai, Tetsuya, Zou, Quan
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 28.03.2022
• Subjects: Algorithms; Drug Development - methods; Drug Discovery - methods; Drug Repositioning; Proteins - chemistry; Software
• ISSN: 1367-4803; 1367-4811; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btac048

Abstract Motivation Drug–target interaction prediction plays an important role in new drug discovery and drug repurposing. Binding affinity indicates the strength of drug–target interactions. Predicting drug–target binding affinity is expected to provide promising candidates for biologists, which can effectively reduce the workload of wet laboratory experiments and speed up the entire process of drug research. Given that, numerous new proteins are sequenced and compounds are synthesized, several improved computational methods have been proposed for such predictions, but there are still some challenges. (i) Many methods only discuss and implement one application scenario, they focus on drug repurposing and ignore the discovery of new drugs and targets. (ii) Many methods do not consider the priority order of proteins (or drugs) related to each target drug (or protein). Therefore, it is necessary to develop a comprehensive method that can be used in multiple scenarios and focuses on candidate order. Results In this study, we propose a method called NerLTR-DTA that uses the neighbor relationship of similarity and sharing to extract features, and applies a ranking framework with regression attributes to predict affinity values and priority order of query drug (or query target) and its related proteins (or compounds). It is worth noting that using the characteristics of learning to rank to set different queries can smartly realize the multi-scenario application of the method, including the discovery of new drugs and new targets. Experimental results on two commonly used datasets show that NerLTR-DTA outperforms some state-of-the-art competing methods. NerLTR-DTA achieves excellent performance in all application scenarios mentioned in this study, and the rm(test)2 values guarantee such excellent performance is not obtained by chance. Moreover, it can be concluded that NerLTR-DTA can provide accurate ranking lists for the relevant results of most queries through the statistics of the association relationship of each query drug (or query protein). In general, NerLTR-DTA is a powerful tool for predicting drug–target associations and can contribute to new drug discovery and drug repurposing. Availability and implementation The proposed method is implemented in Python and Java. Source codes and datasets are available at https://github.com/RUXIAOQING964914140/NerLTR-DTA.