Kortagere Sandhya, Ekins Sean
Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA 19129, USA.
J Pharmacol Toxicol Methods. 2010 Mar-Apr;61(2):67-75. doi: 10.1016/j.vascn.2010.02.005. Epub 2010 Feb 20.
Computational approaches for drug discovery such as ligand-based and structure-based methods, are increasingly seen as an efficient approach for lead discovery as well as providing insights on absorption, distribution, metabolism, excretion and toxicity (ADME/Tox). What is perhaps less well known and widely described are the limitations of the different technologies. We have therefore presented a troubleshooting approach to QSAR, homology modeling, docking as well as hybrid methods. If such computational or cheminformatics methods are to become more widely used by non-experts it is critical that such limitations are brought to the user's attention and addressed during their workflows. This could improve the quality of the models and results that are obtained.
用于药物发现的计算方法,如基于配体和基于结构的方法,越来越被视为一种有效的先导物发现方法,同时也能提供有关吸收、分布、代谢、排泄和毒性(ADME/Tox)的见解。或许鲜为人知且未被广泛描述的是不同技术的局限性。因此,我们提出了一种针对定量构效关系(QSAR)、同源建模、对接以及混合方法的故障排除方法。如果此类计算或化学信息学方法要被非专家更广泛地使用,那么至关重要的是,要让用户注意到这些局限性,并在他们的工作流程中加以解决。这可以提高所获得模型和结果的质量。