Progress Analysis of Compound-Protein Interactions Prediction Algorithms
Drug application has greatly improved the quality of human life.The effectiveness of drug is a key factor in drug discovery process,and is determined by identifying drug-target interactions.However,it is a very expensive,time-consuming and challenging task to analyse and determine the compound-protein interactions through high-throughput screening experimental methods.The drug discovery research using computational methods are high efficiency and low cost,and it has been paid more and more attention.Com-pared with the wet-lab experiments,the computational prediction methods of compound-protein interactions can provide more accurate and safe potential candidate drug-target pairs for the subsequent biological experiment,and reduce the spending time and cost of biolog-ical experiments in drug discovery process.We review development of compound-protein interactions prediction,as well as the biomed-ical feature data,prediction algorithms,and technologies in the past two decades.This paper analyzes the problems faced in the re-search process such as high-dimensional sparsity of biomedical data and insufficient integration of multi-omics biomedical data,and it will provide valuable information for further research.
Drug discoveryCompoud-protein interactionsBiomedical feature dataHigh-dimensional sparsityData fusion
NETL
NSTL
万方数据
