Abstract
BackgroundIdentifying drug targets is a critical step in pharmacology. Drug phenotypic and chemical indexes are two important indicators in this field. However, in previous studies, the indexes were always isolated and the candidate proteins were often limited to a small subset of the human genome.Methodology/Principal FindingsBased on the correlations observed in pharmacological and genomic spaces, we develop a computational framework, drugCIPHER, to infer drug-target interactions in a genome-wide scale. Three linear regression models are proposed, which respectively relate drug therapeutic similarity, chemical similarity and their combination to the relevance of the targets on the basis of a protein-protein interaction network. Typically, the model integrating both drug therapeutic similarity and chemical similarity, drugCIPHER-MS, achieved an area under the Receiver Operating Characteristic (ROC) curve of 0.988 in the training set and 0.935 in the test set. Based on drugCIPHER-MS, a genome-wide map of drug biological fingerprints for 726 drugs is constructed, within which unexpected drug-drug relations emerged in 501 cases, implying possible novel applications or side effects.Conclusions/SignificanceOur findings demonstrate that the integration of phenotypic and chemical indexes in pharmacological space and protein-protein interactions in genomic space can not only speed the genome-wide identification of drug targets but also find new applications for the existing drugs.
Highlights
Identification of drug targets is one of the major tasks in drug discovery [1]
We extracted 726 Food and Drug Administration (FDA) approved drugs that had at least one known Anatomic Therapeutic Chemical (ATC) code and known chemical structure information from DrugBank [25] as our reference set. This set was composed of 1176 drug-ATC code interactions and 2225 drug-target interactions. 678 drugs were found with known targets
By investigating the relations between drug Therapeutic Similarity (TS) and drug chemical similarity (CS), we demonstrated that TS and CS played complementary roles to each other in pharmacological space
Summary
Identification of drug targets is one of the major tasks in drug discovery [1]. In recent years, drug phenotypic effects and chemical structures have been used to infer drug-target interactions. Phenotypic effect-based approaches are based on the various phenotypic responses, such as expression profiles and side effects, to external compounds [2,3,4,5] Such studies treat the biological system as a whole, and associate one drug to other drugs which have similar biological activity or genes with related phenotypic outcomes. On the assumption that structurally similar drugs tend to bind similar proteins, another kind of study using chemical structure-based approaches [6,7,8], especially integrating drug chemical similarity and protein sequence or structure information [9,10,11], has shown lots of encouraging results These studies demonstrate that drug chemical structure information is a good indicator for drug biological activity [12]. In previous studies, the indexes were always isolated and the candidate proteins were often limited to a small subset of the human genome
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