Tresadern Gary, Bemporad Daniele, Howe Trevor
Johnson & Johnson, Pharmaceutical Research & Development, Janssen-Cilag S.A., Calle Jarama, 75, Poligono Industrial, 45007 Toledo, Spain.
J Mol Graph Model. 2009 Jun-Jul;27(8):860-70. doi: 10.1016/j.jmgm.2009.01.003. Epub 2009 Jan 23.
Ligand based virtual screening approaches were applied to the CRF1 receptor. We compared ECFP6 fingerprints, FTrees, Topomers, Cresset FieldScreen, ROCS OpenEye shape Tanimoto, OpenEye combo-score and OpenEye electrostatics. The 3D methods OpenEye Shape Tanimoto, combo-score and Topomers performed the best at separating actives from inactives in retrospective experiments. By virtue of their higher enrichment the same methods identified more active scaffolds. However, amongst a given number of active compounds the Cresset and OpenEye electrostatic methods contained more scaffolds and returned ranked compounds with greater diversity. A selection of the methods were employed to recommend compounds for screening in a prospective experiment. New CRF1 actives antagonists were found. The new actives contained different underlying chemical architecture to the query molecules, results indicative of successful scaffold-hopping.
基于配体的虚拟筛选方法被应用于促肾上腺皮质激素释放因子1（CRF1）受体。我们比较了ECFP6指纹、FTrees、Topomers、Cresset FieldScreen、ROCS OpenEye形状Tanimoto系数、OpenEye组合评分和OpenEye静电方法。在回顾性实验中，3D方法OpenEye形状Tanimoto系数、组合评分和Topomers在区分活性化合物和非活性化合物方面表现最佳。由于它们具有更高的富集度，相同的方法鉴定出了更多的活性骨架。然而，在给定数量的活性化合物中，Cresset和OpenEye静电方法包含更多的骨架，并且返回的排名化合物具有更大的多样性。在一项前瞻性实验中，选用了一些方法来推荐用于筛选的化合物。发现了新的CRF1活性拮抗剂。新的活性化合物具有与查询分子不同的潜在化学结构，结果表明成功实现了骨架跃迁。
