Institute for Theoretical Chemistry, Ulm University, Albert-Einstein-Allee 11, 89069 Ulm, Germany.
J Phys Chem B. 2013 Jul 11;117(27):8075-84. doi: 10.1021/jp402719k. Epub 2013 Jun 25.
Correctly ranking protein-ligand interactions with respect to overall free energy of binding is a grand challenge for virtual drug design. Here we compare the performance of various quantum chemical approaches for tackling this so-called "scoring" problem. Relying on systematically generated benchmark sets of large protein/ligand model complexes based on the PDBbind database, we show that the performance depends first of all on the general level of theory. Comparing classical molecular mechanics (MM), semiempirical quantum mechanical (SQM), and density functional theory (DFT) based methods, we find that enhanced SQM approaches perform very similar to DFT methods and substantially different from MM potentials.
正确地对蛋白质-配体相互作用进行整体结合自由能排序是虚拟药物设计的一个重大挑战。在这里,我们比较了各种量子化学方法在解决这个所谓的“评分”问题方面的性能。我们依靠基于 PDBbind 数据库的大规模蛋白质/配体模型复合物的系统生成基准集,表明性能首先取决于理论的一般水平。通过比较经典的分子力学(MM)、半经验量子力学(SQM)和基于密度泛函理论(DFT)的方法,我们发现增强的 SQM 方法的性能与 DFT 方法非常相似,而与 MM 势能则有很大的不同。