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对接多个口袋或配体场进行筛选、活性预测和骨架跃迁。

Docking to multiple pockets or ligand fields for screening, activity prediction and scaffold hopping.

机构信息

Bioinformatics, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.

出版信息

Future Med Chem. 2014;6(16):1741-55. doi: 10.4155/fmc.14.113.

Abstract

BACKGROUND

Two recent technological advances dramatically reducing the rate of false-negatives in activity prediction by docking flexible 3D models of compounds include multi-conformational docking (mPockDock) and the docking of candidates to atomic property fields derived by co-crystallized ligands (mApfDock).

RESULTS

The mApfDock and mPockDock provide the AUC of 90.4 and 83.8%, respectively. The mApfDock gave better performance when compounds required large induced-fit pocket changes unseen in crystallography, whereas the mPockDock is superior when the co-crystallized ligands do not represent sufficient chemical and binding location diversity.

CONCLUSION

Both approaches proved to be efficient for scaffold hopping; they are complementary when the coverage of the co-crystallized complexes is poor but become convergent when the complexes are diverse enough.

摘要

背景

最近的两项技术进步极大地降低了化合物柔性 3D 模型对接时假阴性的发生率,包括多构象对接(mPockDock)和候选物对接到由共结晶配体衍生的原子性质场(mApfDock)。

结果

mApfDock 和 mPockDock 的 AUC 分别为 90.4%和 83.8%。当化合物需要在晶体学中未见的大诱导契合口袋变化时,mApfDock 的性能更好,而当共结晶配体不能代表足够的化学和结合位置多样性时,mPockDock 则更优越。

结论

两种方法都被证明对支架跳跃很有效;当共结晶复合物的覆盖率较差时,它们是互补的,但当复合物足够多样化时,它们就会趋同。

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