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

Background:Computational approaches for accurate prediction of drug interactions,such as drug-drug 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.

IRIS:A method for predicting in vivo RNA secondary structures using PARIS data

Background:RNA secondary structures play a pivotal role in posttranscriptional regulation and the functions of non-coding RNAs,yet in vivo RNA secondary structures remain enigmatic.PARIS(Psoralen Analysis of RNA Interactions and Structures)is a recently developed high-throughput sequencing-based approach that enables direct capture of RNA duplex structures in vivo.However,the existence of incompatible,fuzzy pairing information obstructs the integration of PARIS data with the existing tools for reconstructing RNA secondary structure models at the single-base resolution.Methods:We introduce IRIS,a method for predicting RNA secondary structure ensembles based on PARIS data.IRIS generates a large set of candidate RNA secondary structure models under the guidance of redistributed PARIS reads and then uses a Bayesian model to identify the optimal ensemble,according to both thermodynamic principles and PARIS data.Results:The predicted RNA structure ensembles by IRIS have been verified based on evolutionary conservation information and consistency with other experimental RNA structural data.HIS is implemented in Python and freely available at http://iris.zhanglab.net.Conclusion:IRIS capitalizes upon PARIS data to improve the prediction of in vivo RNA secondary structure ensembles.We expect that IRIS will enhance the application of the PARIS technology and shed more insight on in vivo RNA secondary structures.