College of Computer Science, Shenyang Aerospace University, Shenyang, 110136, China.
School of Mathematics, Liaoning University, Shenyang, 110036, China.
Curr Protein Pept Sci. 2019;20(6):492-494. doi: 10.2174/1389203720666190123164310.
In the medical field, drug-target interactions are very important for the diagnosis and treatment of diseases, they also can help researchers predict the link between biomolecules in the biological field, such as drug-protein and protein-target correlations. Therefore, the drug-target research is a very popular study in both the biological and medical fields. However, due to the limitations of manual experiments in the laboratory, computational prediction methods for drug-target relationships are increasingly favored by researchers. In this review, we summarize several computational prediction models of the drug-target connections during the past two years, and briefly introduce their advantages and shortcomings. Finally, several further interesting research directions of drug-target interactions are listed.
在医学领域,药物-靶标相互作用对于疾病的诊断和治疗非常重要,它们还可以帮助研究人员预测生物领域中生物分子之间的联系,如药物-蛋白和蛋白-靶标相关性。因此,药物-靶标研究在生物和医学领域都是非常热门的研究方向。然而,由于实验室中手动实验的局限性,药物-靶标关系的计算预测方法越来越受到研究人员的青睐。在这篇综述中,我们总结了过去两年中几种药物-靶标连接的计算预测模型,并简要介绍了它们的优缺点。最后,列出了几个药物-靶标相互作用的进一步有趣的研究方向。