Abstract
BackgroundBecause drug–drug interactions (DDIs) may cause adverse drug reactions or contribute to complex-disease treatments, it is important to identify DDIs before multiple-drug medications are prescribed. As the alternative of high-cost experimental identifications, computational approaches provide a much cheaper screening for potential DDIs on a large scale manner. Nevertheless, most of them only predict whether or not one drug interacts with another, but neglect their enhancive (positive) and depressive (negative) changes of pharmacological effects. Moreover, these comprehensive DDIs do not occur at random, but exhibit a weakly balanced relationship (a structural property when considering the DDI network), which would help understand how high-order DDIs work.ResultsThis work exploits the intrinsically structural relationship to solve two tasks, including drug community detection as well as comprehensive DDI prediction in the cold-start scenario. Accordingly, we first design a balance regularized semi-nonnegative matrix factorization (BRSNMF) to partition the drugs into communities. Then, to predict enhancive and degressive DDIs in the cold-start scenario, we develop a BRSNMF-based predictive approach, which technically leverages drug-binding proteins (DBP) as features to associate new drugs (having no known DDI) with other drugs (having known DDIs). Our experiments demonstrate that BRSNMF can generate the drug communities, which exhibit more reasonable sizes, the property of weak balance as well as pharmacological significances. Moreover, they demonstrate the superiority of DBP features and the inspiring ability of the BRSNMF-based predictive approach on comprehensive DDI prediction with 94% accuracy among top-50 predicted enhancive and 86% accuracy among bottom-50 predicted degressive DDIs.ConclusionsOwing to the regularization of the weak balance property of the comprehensive DDI network into semi-nonnegative matrix factorization, our proposed BRSNMF is able to not only generate better drug communities but also provide an inspiring comprehensive DDI prediction in the cold-start scenario.
Highlights
When two or more drugs are taken together, their pharmacological effects or behaviors would be unexpectedly influenced by each other [1]
balance regularized semi-nonnegative matrix factorization (BRSNMF)‐based approaches for predicting potential comprehensive Drug–Drug Interaction (DDI) of new drugs we show how to make use of BRSNMF to predict potential comprehensive DDIs focusing on the scenario of DDI prediction between ‘new drugs’ and ‘approved drugs’ as the prediction problem is known to be difficult if new drugs are involved (Fig. 2a)
DB_V4 and DB_V5_ Ex, are built according to the version of DrugBank as we need to use known DDIs to validate the accuracy of our prediction
Summary
When two or more drugs are taken together, their pharmacological effects or behaviors would be unexpectedly influenced by each other [1]. There exist ~ 15 DDIs out of every 100 drug pairs on average among approved small molecular drugs in DrugBank [2]. They would put patients, who are treated with multiple-drug medications, in an unsafe situation [3,4,5,6]. Most of them only predict whether or not one drug interacts with another, but neglect their enhancive (positive) and depressive (negative) changes of pharmacological effects These comprehensive DDIs do not occur at random, but exhibit a weakly balanced relationship (a structural property when considering the DDI network), which would help understand how high-order DDIs work
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