Department of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa, 214-8571, Japan.
Mol Inform. 2021 Mar;40(3):e2000123. doi: 10.1002/minf.202000123. Epub 2020 Sep 29.
In three-dimensional (3D)-quantitative structure-activity relationship (QSAR) analysis, the chemical structure of a studied molecule is typically optimized assuming its presence in a vacuum environment. However, in practical scenarios, the environment of even the most stable molecules contains water, proteins, and other species; therefore, their actual structures significantly differ from those in vacuum and have multiple structures. Herein, both two-dimensional and 3D molecular descriptors, which accepted the existence of multiple conformers, were calculated, and a conformer-based 3D-QSAR model (C3D-QSAR) that considered the chemical structures of conformers was developed. The prediction accuracy of the C3D-QSAR method determined by analyzing the data sets obtained for the angiotensin-converting enzyme and dihydrofolate reductase inhibitors was found to be higher than those of the existing QSAR models.
在三维（3D）定量构效关系（QSAR）分析中，通常假设研究分子的化学结构存在于真空环境中。然而，在实际情况下，即使是最稳定的分子的环境也包含水、蛋白质和其他物质；因此，它们的实际结构与真空环境中的结构有很大的不同，并且具有多种结构。在此，本文计算了同时接受多种构象存在的二维和三维分子描述符，并开发了一种基于构象的 3D-QSAR 模型（C3D-QSAR），该模型考虑了构象的化学结构。通过分析为血管紧张素转化酶和二氢叶酸还原酶抑制剂获得的数据组，确定 C3D-QSAR 方法的预测准确性高于现有 QSAR 模型的预测准确性。
