DDR: efficient computational method to predict drug-target interactions using graph mining and machine learning approaches

Abstract

Motivation: Finding computationally drug-target interactions (DTIs) is a convenient strategy to identify new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer the high false positive prediction rate.

Results: We developed DDR, a novel method that improves the DTI prediction accuracy. DDR is based on the use of a heterogeneous graph that contains known DTIs with multiple similarities between drugs and multiple similarities between target proteins. DDR applies non-linear similarity fusion method to combine different similarities. Before fusion, DDR performs a pre-processing step where a subset of similarities is selected in a heuristic process to obtain an optimized combination of similarities. Then, DDR applies a random forest model using different graph-based features extracted from the DTI heterogeneous graph. Using 5-repeats of 10-fold cross-validation, three testing setups, and the weighted average of area under the precision-recall curve (AUPR) scores, we show that DDR significantly reduces the AUPR score error relative to the next best start-of-the-art method for predicting DTIs by 34% when the drugs are new, by 23% when targets are new and by 34% when the drugs and the targets are known but not all DTIs between them are not known. Using independent sources of evidence, we verify as correct 22 out of the top 25 DDR novel predictions. This suggests that DDR can be used as an efficient method to identify correct DTIs.

Availability and implementation: The data and code are provided at https://bitbucket.org/RSO24/ddr/.

Contact: vladimir.bajic@kaust.edu.sa.

Supplementary information: Supplementary data are available at Bioinformatics online.

Fig. 1.

Flowchart of DDR method. DDR consists of several steps including: (i) Similarity selection, where a subset of similarity measures is selected in a heuristic process. (ii) Similarity fusion, with the goal to combine the selected similarity measures into one final composite similarity that combines information from similarities determined in (i). (iii) Path-category-based feature extraction, where the feature vector corresponds to drug and target protein pairs, i.e. for $(d_i,t_j)$ pair, features are determined as the vector composed of the 12 (i, j) elements obtained by two graph-based scores, namely, n1(h, i, j) and n2(h, i, j) for each specific path-category C_h,h = 1, 2, …, 6. (iv) Building DTI prediction model using RF, where both positive and negative data are provided; positive data contain known links between drugs and target proteins and represent positive labels, while negative data contain unknown DTI links that are treated as negative labels

Fig. 2.

Comparison results (in terms of AUPR scores) of DDR with the five state of the art methods (DNILMF, NRLMF, KRONRLS-MKL, COSINE and BLM-NII) using 5-repeats of 10-fold CV. Results are obtained under three prediction tasks (S_P, S_D and S_T) over all datasets (NR, GPCR, IC, E and DrugBank_FDA) used in this study. The results for DNILMF, NRLMF, KRONRLS-MKL, COSINE and BLM-NII are obtained using the best parameters reported in the respective publications