Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing

摘要

Target-based screening is one of the major approaches in drug discovery. Besides theintended target, unexpected drug off-target interactions often occur, and many of themhave not been recognized and characterized. The off-target interactions can be responsiblefor either therapeutic or side effects. Thus, identifying the genome-wide off-targets of leadcompounds or existing drugs will be critical for designing effective and safe drugs, and providing new opportunities for drug repurposing. Although many computational methodshave been developed to predict drug-target interactions, they are either less accurate thanthe one that we are proposing here or computationally too intensive, thereby limiting theircapability for large-scale off-target identification. In addition, the performances of mostmachine 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 isbased 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 ina reliable, extensive, and cross-gene family benchmark, REMAP outperforms the state-ofthe-art methods. Furthermore, REMAP is highly scalable. It can screen a dataset of 200thousands 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 andbenchmark are available at https://github.com/hansaimlim/REMAP.