DeepDrug: A general graph‐based deep learning framework for drug‐drug interactions and drug‐target interactions prediction

Abstract

Background: Computational approaches for accurate prediction of drug interactions, such as drugdrug interactions (DDIs) and drug-target interactions (DTIs), are highly demanded for biochemical researchers. Despite the fact that many methods have been proposed and developed to predict DDIs and DTIs respectively, their success is still limited due to a lack of systematic evaluation of the intrinsic properties embedded in the corresponding chemical structure. Methods: In this paper, we develop DeepDrug, a deep learning framework for overcoming the above limitation by using residual graph convolutional networks (Res-GCNs) and convolutional networks (CNNs) to learn the comprehensive structure-and sequence-based representations of drugs and proteins. Results: DeepDrug outperforms state-of-the-art methods in a series of systematic experiments, including binary-class DDIs, multi-class/multi-label DDIs, binary-class DTIs classification and DTIs regression tasks. Furthermore, we visualize the structural features learned by DeepDrug Res-GCN module, which displays compatible and accordant patterns in chemical properties and drug categories, providing additional evidence to support the strong predictive power of DeepDrug. Ultimately, we apply DeepDrug to perform drug repositioning on the whole DrugBank database to discover the potential drug candidates against SARS-CoV-2, where 7 out of 10 top-ranked drugs are reported to be repurposed to potentially treat coronavirus disease 2019 (COVID-19). Conclusions:To sum up, we believe that DeepDrug is an efficient tool in accurate prediction of DDIs and DTIs and provides a promising insight in understanding the underlying mechanism of these biochemical relations.

Graph Neural Network and BERT Model for Antimalarial Drug Predictions Using Plasmodium Potential Targets

“…Drug-target links information has been widely used to study various drug-related problems, such as drug and target interactions' prediction [12,13,27,33,58,59], drug anatom-ical therapeutic chemical (ATC) classifiers of drugs [60,61], and adverse reactions' prediction [62,63]. Here, the link information between the drugs was identified according to the combination of antimalarial drugs and the new targets introduced in this study.…”

“…There has been a shift in the discovery of antimalarial drugs from phenotypic screening to target-based approaches, as more potential drug targets have been validated in Plasmodium species [4]. Given the high attrition rate, high demand for new drugs, and enormous cost and time-consuming nature of drug discovery [12][13][14][15][16], it is essential to select the targets that are the most likely to deliver progressable drug candidates. Target-based drug discovery is the dominant paradigm of drug discovery [17].…”

“…These frameworks have shown their effectiveness in various applications, such as molecular properties prediction [40], widespread application in predicting DTIs [12], and the prediction of protein function [41] within the realm of biochemical problems where the interactions can be represented as graph-like structured data. Specifically, GCNs have found application in addressing pharmacological similarities by considering both sequential and structural properties [12]. The graph representations of biochemical entities have proven capable of capturing structural features akin to Euclidean ones, eliminating the need for extensive feature engineering [42,43].…”

Predict lncRNA-drug associations based on graph neural network

“…Recently, due to the breakthroughs in deep learning and the huge improvements in computing power, models based on deep learning, particularly those employing GNNs, have been applied in multiple bioinformatics-related tasks, such as lncRNA-disease association prediction, and drug-target interaction prediction (Xuan et al, 2019;Chen et al, 2020;Kumar Shukla et al, 2020;Zhao et al, 2023). Yin et al (2023) proposed a general framework using residual GCN and CNNs to predict drug-target interactions. Wang and Zhong (2022) proposed a method (gGATLDA) to identify potential lncRNA-disease associations based on graph attention networks (GAT).…”

A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical networks

“…Therefore, identifying the interactions between drug-target and drug-disease is of great significance for drug development. At the same time, the interaction between drug-target or drug-disease also provides a higher-level point of view for better understanding the drug-side effects and drug-drug associations [3]. In recent years, drugrelated molecular interaction networks, e.g., drug-drug interactions (DDIs), drug-target interactions (DTIs), drug-disease interactions (DDiIs), and drug-side-effect interactions (DSIs) are continuously expanded, which have greatly increased the computational power used to aid drug discovery (Table A in S1 Text displays the abbreviation list in this study for ease of reading).…”