Abstract
Target-based screening is one of the major approaches in drug discovery. Besides the intended target, unexpected drug off-target interactions often occur, and many of them have not been recognized and characterized. The off-target interactions can be responsible for either therapeutic or side effects. Thus, identifying the genome-wide off-targets of lead compounds or existing drugs will be critical for designing effective and safe drugs, and providing new opportunities for drug repurposing. Although many computational methods have been developed to predict drug-target interactions, they are either less accurate than the one that we are proposing here or computationally too intensive, thereby limiting their capability for large-scale off-target identification. In addition, the performances of most machine learning based algorithms have been mainly evaluated to predict off-target interactions in the same gene family for hundreds of chemicals. It is not clear how these algorithms perform in terms of detecting off-targets across gene families on a proteome scale. Here, we are presenting a fast and accurate off-target prediction method, REMAP, which is based on a dual regularized one-class collaborative filtering algorithm, to explore continuous chemical space, protein space, and their interactome on a large scale. When tested in a reliable, extensive, and cross-gene family benchmark, REMAP outperforms the state-of-the-art methods. Furthermore, REMAP is highly scalable. It can screen a dataset of 200 thousands chemicals against 20 thousands proteins within 2 hours. Using the reconstructed genome-wide target profile as the fingerprint of a chemical compound, we predicted that seven FDA-approved drugs can be repurposed as novel anti-cancer therapies. The anti-cancer activity of six of them is supported by experimental evidences. Thus, REMAP is a valuable addition to the existing in silico toolbox for drug target identification, drug repurposing, phenotypic screening, and side effect prediction. The software and benchmark are available at https://github.com/hansaimlim/REMAP.
Highlights
Conventional one-drug-one-gene drug discovery and drug development is a time-consuming and expensive process
It was noticeable that REMAP performed significantly better than PRW when there was at least one known target for a chemical whose targets are predicted (Figs 3 and 4)
REMAP is applicable to chemicals that are structurally distant to the chemicals already in the dataset
Summary
Conventional one-drug-one-gene drug discovery and drug development is a time-consuming and expensive process. It suffers from high attrition rate and possible unexpected post-market withdrawal [1]. The off-target interaction may lead to adverse drug reactions (ADRs) [3], as demonstrated by the deadly side effect of a Fatty Acid Amide Hydrolase (FAAH) inhibitor in a recent clinical trial [4]. The off-target interaction may be therapeutically useful, providing opportunities for drug repurposing and polypharmacology [2]. Identifying off-target interactions is an important step in drug discovery and development in order to reduce the drug attrition rate and to accelerate the drug discovery and development process, and to make safer and more affordable drugs
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