Feher Miklos
Campbell Family Institute for Breast Cancer Research, University Health Network, Toronto Medical Discovery Tower, 101 College Street, Suite 5-361, Toronto, Ontario, M5G 1L7, Canada.
Drug Discov Today. 2006 May;11(9-10):421-8. doi: 10.1016/j.drudis.2006.03.009.
This article reviews the application of consensus scoring for cases when the target 3D structure is known. Comparing the performance of different methods is not a trivial task, and it appears that consensus scoring usually substantially improves virtual screening performance, contributing to better enrichments. It also seems to improve--albeit less dramatically--the prediction of bound conformations and poses. The prediction of binding energies is still rather inaccurate and although consensus scoring generally improves these predictions, more development is required before it can be used for this purpose in routine lead optimization.
本文回顾了在已知目标三维结构的情况下，一致性评分的应用。比较不同方法的性能并非易事，而且似乎一致性评分通常能大幅提高虚拟筛选性能，有助于实现更好的富集效果。它似乎还能改善（尽管效果不太显著）结合构象和姿势的预测。结合能的预测仍然相当不准确，虽然一致性评分总体上能改善这些预测，但在其可用于常规先导化合物优化之前，仍需要更多的发展。
