生物信息学
药物发现
计算生物学
药物开发
药效团
计算机科学
药品
仿形(计算机编程)
药理学
生化工程
生物信息学
生物
工程类
生物化学
基因
操作系统
作者
Nour El-Huda Daoud,Pobitra Borah,Pran Kishore Deb,Katharigatta N. Venugopala,Wafa Hourani,Muhammed Alzweiri,Sanaa K. Bardaweel,Vinod K. Tiwari
标识
DOI:10.2174/1389200222666210705122913
摘要
In the drug discovery setting, undesirable ADMET properties of a pharmacophore with good predictive power obtained after a tedious drug discovery and development process may lead to late-stage attrition. The earlystage ADMET profiling has brought a new dimension to lead drug development. Although several high-throughput in vitro models are available for ADMET profiling, the in silico methods are gaining more importance because of their economic and faster prediction ability without the requirements of tedious and expensive laboratory resources. Nonetheless, in silico ADMET tools alone are not accurate, and therefore, ideally adopted along with in vitro and or in vivo methods in order to enhance the predictability power. This review summarizes the significance and challenges associated with the application of in silico tools as well as the possible scope of in vitro models for integration to improve the ADMET predictability power of these tools.
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