Predicting Drug-Target Interactions with Neighbor Interaction Information and Discriminative Low-rank Representation.
Inferring drug-target interaction (DTI) candidates for new drugs or targets without any interaction information is a critical challenge for modern drug design and discovery. Results from existing DTI inference methods indicate that these approaches necessitate further improvement.



In this paper, we developed a novel DTI identification model (PreNNDS) by integrating Neighbor interaction profiles, Nonnegative matrix factorization, Discriminative low-rank representation, and Sparse representation classification into a unified framework.



AUPR values on four types of datasets show that PreNNDS can efficiently identify potential DTIs for new drugs or targets. We listed predicted top 20 drugs interacting with hsa1132 and hsa1124 and top 20 targets interacting with D00255 and D00195.



PreNNDS can be applied to identify multi-target drugs and multi-drug resistance proteins, as well as to provide clues for microRNA-disease and gene-disease association prediction.
Key Laboratory for Embedded and Network Computing of Hunan Province, the College of Information Science and Engineering, Hunan University, Changsha Hunan, 410082, China|Key Laboratory breeding base of Hunan Oriented Fundamental and Applied Research of Innovative Pharmaceutics, Hunan, Changsha 410219, China|College of Information Engineering, Changsha Medical University, Changsha Hunan, 410219, China
Lihong
L
Peng
Key Laboratory for Embedded and Network Computing of Hunan Province, the College of Information Science and Engineering, Hunan University, Changsha Hunan, 410082, China
Bo
B
Liao
Key Laboratory for Embedded and Network Computing of Hunan Province, the College of Information Science and Engineering, Hunan University, Changsha Hunan, 410082, China
Wen
W
Zhu
Key Laboratory for Embedded and Network Computing of Hunan Province, the College of Information Science and Engineering, Hunan University, Changsha Hunan, 410082, China
Zejun
Z
Li
D000465:Algorithms; D003198:Computer Simulation; D015195:Drug Design; D055808:Drug Discovery; D004351:Drug Resistance; D008958:Models, Molecular; D027425:ATP-Binding Cassette, Sub-Family C Proteins / Q000737:chemistry; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D011485:Protein Binding; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D013329:Structure-Activity Relationship
D027425:ATP-Binding Cassette, Sub-Family C Proteins; D004364:Pharmaceutical Preparations; D011506:Proteins
Drug-target interaction; discriminative
low-rank representation; neighbor interaction profile; new drugs or targets; nonnegative matrix factorization; sparse representation classification