Lee Pil H, Cucurull-Sanchez Lourdes, Lu Jing, Du Yuhua J
Computer-Assisted Drug Discovery, Pfizer Global Research and Development, Ann Arbor, MI 48105, USA.
J Comput Aided Mol Des. 2007 Dec;21(12):665-73. doi: 10.1007/s10822-007-9124-0. Epub 2007 Jun 29.
We developed highly predictive classification models for human liver microsomal (HLM) stability using the apparent intrinsic clearance (CL(int, app)) as the end point. HLM stability has been shown to be an important factor related to the metabolic clearance of a compound. Robust in silico models that predict metabolic clearance are very useful in early drug discovery stages to optimize the compound structure and to select promising leads to avoid costly drug development failures in later stages. Using Random Forest and Bayesian classification methods with MOE, E-state descriptors, ADME Keys, and ECFP_6 fingerprints, various highly predictive models were developed. The best performance of the models shows 80 and 75% prediction accuracy for the test and validation sets, respectively. A detailed analysis of results will be shown, including an assessment of the prediction confidence, the significant descriptors, and the application of these models to drug discovery projects.
我们以表观内在清除率（CL(int, app)）为终点，开发了用于预测人肝微粒体（HLM）稳定性的高度预测性分类模型。HLM稳定性已被证明是与化合物代谢清除相关的一个重要因素。能够预测代谢清除率的稳健计算机模型在药物发现早期阶段对于优化化合物结构以及选择有前景的先导化合物以避免后期昂贵的药物开发失败非常有用。使用带有MOE、E态描述符、ADME键和ECFP_6指纹的随机森林和贝叶斯分类方法，开发了各种高度预测性模型。这些模型的最佳性能分别显示测试集和验证集的预测准确率为80%和75%。将展示结果的详细分析，包括预测置信度评估、重要描述符以及这些模型在药物发现项目中的应用。
