Abstract
BackgroundFast and accurate identification of potential drug candidates against therapeutic targets (i.e., drug–target interactions, DTIs) is a fundamental step in the early drug discovery process. However, experimental determination of DTIs is time-consuming and costly, especially for testing the associations between the entire chemical and genomic spaces. Therefore, computationally efficient algorithms with accurate predictions are required to achieve such a challenging task. In this work, we design a new chemoinformatics approach derived from neighbor-based collaborative filtering (NBCF) to infer potential drug candidates for targets of interest. One of the fundamental steps of NBCF in the application of DTI predictions is to accurately measure the similarity between drugs solely based on the DTI profiles of known knowledge. However, commonly used similarity calculation methods such as COSINE may be noise-prone due to the extremely sparse property of the DTI bipartite network, which decreases the model performance of NBCF. We herein propose three strategies to remedy such a dilemma, which include: (1) adopting a positive pointwise mutual information (PPMI)-based similarity metric, which is noise-immune to some extent; (2) performing low-rank approximation of the original prediction scores; (3) incorporating auxiliary (complementary) information to produce the final predictions.ResultsWe test the proposed methods in three benchmark datasets and the results indicate that our strategies are helpful to improve the NBCF performance for DTI predictions. Comparing to the prior algorithm, our methods exhibit better results assessed by a recall-based evaluation metric.ConclusionsA new chemoinformatics approach with improved strategies was successfully developed to predict potential DTIs. Among them, the model based on the sparsity resistant PPMI similarity metric exhibits the best performance, which may be helpful to researchers for identifying potential drugs against therapeutic targets of interest, and can also be applied to related research such as identifying candidate disease genes.
Highlights
Fast and accurate identification of potential drug candidates against therapeutic targets is a fundamental step in the early drug discovery process
Instead of adopting the commonly used evaluation metrics [i.e., area under curve (AUC) and area under precision-recall (AUPR) curve], we introduce a recall-based metric, namely mean percentile ranking (MPR), which is under-studied in drug–target interactions (DTIs) predictions [17] but routinely used in the recommender system studies [18, 24]
We conclude that the proposed neighbor-based collaborative filtering (NBCF) algorithm has generated promising results which largely outperform the random recommendation accuracy (i.e., 0.5) in terms of MPR [18, 24], and positive pointwise mutual information (PPMI)-based NCBF significantly outperforms both the COSINE-based and TANIMOTObased counterparts (P < 0.01, t test)
Summary
We test the proposed methods in three benchmark datasets and the results indicate that our strategies are helpful to improve the NBCF performance for DTI predictions. Comparing to the prior algorithm, our methods exhibit better results assessed by a recall-based evaluation metric
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