在药物发现中，对结合自由能的可靠预测至关重要。将分子力学力场与连续溶剂模型相结合的方法由于其高精度和相对较好的计算效率而变得流行。在这项研究中，我们研究了分子力学广义玻恩表面积(MM-GBSA)的性能，分子力学泊松-玻尔兹曼表面积(MM-PBSA)，和溶剂化相互作用能（SIE）的虚拟筛选效率和预测实验确定的五个不同蛋白质靶标的结合亲和力的能力。蛋白质-配体复合物是通过两种在虚拟筛选中很重要的不同方法衍生的：分子对接和基于配体的相似性搜索方法。结果表明，不同的结合能计算方法之间存在显著差异。然而,分子动力学模拟的长度对于结果的准确性并不重要。
In drug discovery the reliable prediction of binding free energies is of crucial importance. Methods that combine molecular mechanics force fields with continuum solvent models have become popular because of their high accuracy and relatively good computational efficiency. In this research we studied the performance of molecular mechanics generalized Born surface area (MM-GBSA), molecular mechanics Poisson-Boltzmann surface area (MM-PBSA), and solvated interaction energy (SIE) both in their virtual screening efficiency and their ability to predict experimentally determined binding affinities for five different protein targets. The protein-ligand complexes were derived with two different approaches important in virtual screening: molecular docking and ligand-based similarity search methods. The results show significant differences between the different binding energy calculation methods. However, the length of the molecular dynamics simulation was not of crucial importance for accuracy of results.
