Pires Douglas E V, Blundell Tom L, Ascher David B
†Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Sanger Building, Cambridge, Cambridgshire CB2 1GA, U.K.
‡Centro de Pesquisas René Rachou, Fundação Oswaldo Cruz, Belo Horizonte 30190-002, Brazil.
J Med Chem. 2015 May 14;58(9):4066-72. doi: 10.1021/acs.jmedchem.5b00104. Epub 2015 Apr 22.
Drug development has a high attrition rate, with poor pharmacokinetic and safety properties a significant hurdle. Computational approaches may help minimize these risks. We have developed a novel approach (pkCSM) which uses graph-based signatures to develop predictive models of central ADMET properties for drug development. pkCSM performs as well or better than current methods. A freely accessible web server (http://structure.bioc.cam.ac.uk/pkcsm), which retains no information submitted to it, provides an integrated platform to rapidly evaluate pharmacokinetic and toxicity properties.
药物研发的淘汰率很高，不良的药代动力学和安全性是一个重大障碍。计算方法可能有助于将这些风险降至最低。我们开发了一种新方法（pkCSM），该方法使用基于图的特征来开发用于药物研发的中枢ADMET性质的预测模型。pkCSM的表现与当前方法相当或更优。一个可免费访问的网络服务器（http://structure.bioc.cam.ac.uk/pkcsm），它不保留提交给它的任何信息，提供了一个综合平台来快速评估药代动力学和毒性性质。
