Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan.
Curr Pharm Des. 2013;19(12):2174-82. doi: 10.2174/1381612811319120005.
The scoring functions for protein-ligand interactions plays central roles in computational drug design, virtual screening of chemical libraries for new lead identification, and prediction of possible binding targets of small chemical molecules. An ideal scoring function for protein-ligand interactions is expected to be able to recognize the native binding pose of a ligand on the protein surface among decoy poses, and to accurately predict the binding affinity (or binding free energy) so that the active molecules can be discriminated from the non-active ones. Due to the empirical nature of most, if not all, scoring functions for protein-ligand interactions, the general applicability of empirical scoring functions, especially to domains far outside training sets, is a major concern. In this review article, we will explore the foundations of different classes of scoring functions, their possible limitations, and their suitable application domains. We also provide assessments of several scoring functions on weakly-interacting protein-ligand complexes, which will be useful information in computational fragment-based drug design or virtual screening.
蛋白质-配体相互作用的评分函数在计算药物设计、化学文库的虚拟筛选以寻找新的先导化合物以及预测小分子的可能结合靶标方面发挥着核心作用。理想的蛋白质-配体相互作用评分函数应该能够在诱饵构象中识别配体在蛋白质表面上的天然结合构象，并准确预测结合亲和力（或结合自由能），从而能够将活性分子与非活性分子区分开来。由于大多数（如果不是全部）蛋白质-配体相互作用评分函数都具有经验性质，因此经验评分函数的一般适用性，特别是在远远超出训练集的领域，是一个主要关注点。在这篇综述文章中，我们将探讨不同类别的评分函数的基础、它们可能的局限性以及它们的适用领域。我们还对几种评分函数在弱相互作用的蛋白质-配体复合物上的评估，这将是计算片段药物设计或虚拟筛选中有用的信息。
