Kalinowsky Lena, Weber Julia, Balasupramaniam Shantheya, Baumann Knut, Proschak Ewgenij
Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue Str. 9, Frankfurt am Main D-60438, Germany.
Institute of Medicinal and Pharmaceutical Chemistry, University of Technology of Braunschweig, Beethovenstr. 55, Braunschweig D-38106, Germany.
ACS Omega. 2018 May 28;3(5):5704-5714. doi: 10.1021/acsomega.7b01194. eCollection 2018 May 31.
The prediction of protein-ligand interactions and their corresponding binding free energy is a challenging task in structure-based drug design and related applications. Docking and scoring is broadly used to propose the binding mode and underlying interactions as well as to provide a measure for ligand affinity or differentiate between active and inactive ligands. Various studies have revealed that most docking software packages reliably predict the binding mode, although scoring remains a challenge. Here, a diverse benchmark data set of 99 matched molecular pairs (3D-MMPs) with experimentally determined X-ray structures and corresponding binding affinities is introduced. This data set was used to study the predictive power of 13 commonly used scoring functions to demonstrate the applicability of the 3D-MMP data set as a valuable tool for benchmarking scoring functions.
在基于结构的药物设计及相关应用中，预测蛋白质 - 配体相互作用及其相应的结合自由能是一项具有挑战性的任务。对接和打分被广泛用于提出结合模式和潜在相互作用，以及提供配体亲和力的度量或区分活性和非活性配体。各种研究表明，尽管打分仍然是一个挑战，但大多数对接软件包能够可靠地预测结合模式。在此，引入了一个包含99个匹配分子对（3D - MMPs）的多样化基准数据集，这些分子对具有实验测定的X射线结构和相应的结合亲和力。该数据集用于研究13种常用打分函数的预测能力，以证明3D - MMP数据集作为打分函数基准测试的有价值工具的适用性。
