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A hybrid ensemble‐based technique for predicting drug–target interactions
Author(s) -
Sachdev Kanica,
Gupta Manoj Kumar
Publication year - 2020
Publication title -
chemical biology and drug design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 77
eISSN - 1747-0285
pISSN - 1747-0277
DOI - 10.1111/cbdd.13753
Subject(s) - computer science , machine learning , artificial intelligence , drug target , ensemble learning , drug discovery , data mining , bioinformatics , chemistry , biology , biochemistry
Drug–target interaction is the intercommunication between chemical drugs and target proteins to produce any kind of change in the human body. The laboratory experiments conducted to recognize these potential intercommunications are costly and tedious. Various computational methods have been established recently, including the chemogenomic approach for identifying drug–target intercommunication, that integrates the chemical data of the drugs and the genomic properties of the proteins for identifying the interactions between them. This paper proposes a novel technique based on hybrid ensemble to predict drug target interactions. Hybrid ensembles introduce diversity in classification that helps to improve the performance of prediction. The proposed method has been evaluated by comparing the technique with state–of‐the‐art methods on two different databases under three cross‐validation settings. The comparison clearly shows that the technique produced significant improvement in the interaction prediction.