Abstract
Identifying new indications for existing drugs may reduce costs and expedites drug development. Drug-related disease predictions typically combined heterogeneous drug-related and disease-related data to derive the associations between drugs and diseases, while recently developed approaches integrate multiple kinds of drug features, but fail to take the diversity implied by these features into account. We developed a method based on non-negative matrix factorization, DivePred, for predicting potential drug–disease associations. DivePred integrated disease similarity, drug–disease associations, and various drug features derived from drug chemical substructures, drug target protein domains, drug target annotations, and drug-related diseases. Diverse drug features reflect the characteristics of drugs from different perspectives, and utilizing the diversity of multiple kinds of features is critical for association prediction. The various drug features had higher dimensions and sparse characteristics, whereas DivePred projected high-dimensional drug features into the low-dimensional feature space to generate dense feature representations of drugs. Furthermore, DivePred’s optimization term enhanced diversity and reduced redundancy of multiple kinds of drug features. The neighbor information was exploited to infer the likelihood of drug–disease associations. Experiments indicated that DivePred was superior to several state-of-the-art methods for prediction drug-disease association. During the validation process, DivePred identified more drug-disease associations in the top part of prediction result than other methods, benefitting further biological validation. Case studies of acetaminophen, ciprofloxacin, doxorubicin, hydrocortisone, and ampicillin demonstrated that DivePred has the ability to discover potential candidate disease indications for drugs.
Highlights
Developing a new drug is a complex, time-consuming, and expensive process [1,2], which typically proceeds through preliminary compound testing, pre-clinical and animal experiments, clinical research, and Food and Drug Administration (FDA) review, before it yields a new drug that reaches the market after 10–15 years, costing approximately 0.8–1.5 billion dollars [3,4,5,6]
A method based on non-negative matrix factorization, DivePred, was developed to infer the potential associations between drugs and diseases
DivePred captures a variety of information on each drug, including four kinds of drug features and specific features associated with different aspects of the drugs
Summary
Developing a new drug is a complex, time-consuming, and expensive process [1,2], which typically proceeds through preliminary compound testing, pre-clinical and animal experiments, clinical research, and Food and Drug Administration (FDA) review, before it yields a new drug that reaches the market after 10–15 years, costing approximately 0.8–1.5 billion dollars [3,4,5,6]. Several methods for predicting the association of drugs with diseases based on related target genes or gene expression profiles have been proposed [16,17]. The possibility of a drug–disease association can be estimated based on the targeted protein complexes shared by the drugs and diseases [18] and the perturbed genes they have in common [19]. These methods are limited to drugs and diseases with shared genes or proteins
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