Drug-target interaction prediction by integrating heterogeneous information with mutual attention network

Abstract

Background: Identification of drug-target interactions is an indispensable part of drug discovery. While conventional shallow machine learning and recent deep learning methods based on chemogenomic properties of drugs and target proteins have pushed this prediction performance improvement to a new level, these methods are still difficult to adapt to novel structures. Alternatively, large-scale biological and pharmacological data provide new ways to accelerate drug-target interaction prediction.

Methods: Here, we propose DrugMAN, a deep learning model for predicting drug-target interaction by integrating multiplex heterogeneous functional networks with a mutual attention network (MAN). DrugMAN uses a graph attention network-based integration algorithm to learn network-specific low-dimensional features for drugs and target proteins by integrating four drug networks and seven gene/protein networks collected by a certain screening conditions, respectively. DrugMAN then captures interaction information between drug and target representations by a mutual attention network to improve drug-target prediction.

Results: DrugMAN achieved the best performance compared with cheminformation-based methods SVM, RF, DeepPurpose and network-based deep learing methods DTINet and NeoDT in four different scenarios, especially in real-world scenarios. Compared with SVM, RF, deepurpose, DTINet, and NeoDT, DrugMAN showed the smallest decrease in AUROC, AUPRC, and F1-Score from warm-start to Both-cold scenarios. This result is attributed to DrugMAN's learning from heterogeneous data and indicates that DrugMAN has a good generalization ability. Taking together, DrugMAN spotlights heterogeneous information to mine drug-target interactions and can be a powerful tool for drug discovery and drug repurposing.

Keywords: Deep learning; Drug discovery; Drug–target interaction; Heterogeneous network; Self-attention.

Fig. 1

DrugMAN framework. DrugMAN contains two main parts. The first part encodes network-specific drug and target features from heterogeneous drug and gene/protein networks through sequential graph attention networks (GAT). The combined drug and target features (F_dt) are fed into the second part to learn the updated F_dt by the five transformer encoders. The updated F_dt captures interaction information between the drug and the target. Then the drug and target features in the updated F_dt are concatenated to drug–target pair representation (F_pair), which is input to the fully connected classification layer to calculate the drug–target binding probability score

Fig. 2

Comparison of DrugMAN to state-of-the-art methods. We compare the prediction performance of DrugMAN to that of five baselines, including three chemoinformatics models SVM, RF and DeepPurpose, and two network-based models DTINet and NeoDTI. The rows from top to bottom correspond to four scenarios: warm-start, drug-cold, target-cold and both cold, respectively. The columns from left to right correspond to three metrics: the receiver operating characteristic curve, precision-recall curve and F1 Score. The box plots show the median as the center lines and the mean as green triangles for five random runs. The minima and lower percentile represent the worst and second-worst scores. The maxima and upper percentile indicate the best and second-best scores

Fig. 3

Evaluation of different network embedding methods. In DrugMAN, the drug and target embedding module BIONIC is replaced by two other network integration methods: DeepNF and Multi-node2vec. The vertical bars represent the mean value of five random runs, and the black lines are error bars indicating the standard deviation. The dots indicate performance scores in each random run

Fig. 4

Evaluation of DrugMAN based on the drug structure similarity network and the protein sequence similarity network. DrugMAN with only the drug structure similarity network and the protein sequence similarity network as input (DrugMAN_STR) outperforms three state-of-the-art chemoinformatic models but underperforms DrugMAN. The box plots show the median as the center lines and the mean as green triangles. The minima and lower percentile represent the worst and second-worst scores. The maxima and upper percentile indicate the best and second-best scores

Fig. 5

Ablation study for DrugMAN without attention mechanisms. Performance comparison of DrugMAN with and without attention mechanisms (No attention) in different scenarios. The vertical bars represent the mean of five random runs, and the black lines are error bars indicating the standard deviation. The dots indicate performance scores in each random run