Bioinformatics, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.
Future Med Chem. 2014;6(16):1741-55. doi: 10.4155/fmc.14.113.
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).
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.
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.
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）。
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。
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。
