Venhorst Jennifer, Núñez Sara, Terpstra Jan Willem, Kruse Chris G
Solvay Pharmaceuticals, Research Laboratories, Weesp, The Netherlands.
J Med Chem. 2008 Jun 12;51(11):3222-9. doi: 10.1021/jm8001058. Epub 2008 May 1.
A novel scoring algorithm based on molecular interaction fingerprints (IFPs) was comparatively evaluated in its scaffold hopping efficiency against four virtual screening standards (GlideXP, Gold, ROCS, and a Bayesian classifier). Decoy databases for the two targets under examination, adenosine deaminase and retinoid X receptor alpha, were obtained from the Directory of Useful Decoys and were further enriched with approximately 5% of active ligands. Structure and ligand-based methods were used to generate the ligand poses, and a Tanimoto metric was chosen for the calculation of the similarity interaction fingerprint between the reference ligand and the screening database. Database enrichments were found to strongly depend on the pose generator algorithm. In spite of these dependencies, enrichments using molecular IFPs were comparable to those obtained with GlideXP, Gold, ROCS, and the Bayesian classifier. More interestingly, the molecular IFP scoring algorithm outperformed these methods at scaffold hopping enrichment, regardless of the pose generator algorithm.
一种基于分子相互作用指纹(IFP)的新型评分算法,针对四种虚拟筛选标准(GlideXP、Gold、ROCS和贝叶斯分类器),对其骨架跃迁效率进行了比较评估。所研究的两个靶点(腺苷脱氨酶和视黄酸X受体α)的诱饵数据库取自有用诱饵目录,并进一步用约5%的活性配体进行了富集。基于结构和配体的方法用于生成配体构象,并且选择了Tanimoto度量来计算参考配体与筛选数据库之间的相似性相互作用指纹。发现数据库富集强烈依赖于构象生成算法。尽管存在这些依赖性,但使用分子IFP的富集与使用GlideXP、Gold、ROCS和贝叶斯分类器所获得的富集相当。更有趣的是,无论构象生成算法如何,分子IFP评分算法在骨架跃迁富集方面都优于这些方法。