Research Informatics, Computational Chemistry, Aptuit SRL, an Evotec Company, Verona, Italy.
Methods Mol Biol. 2020;2114:285-305. doi: 10.1007/978-1-0716-0282-9_18.
In recent years, there has been an increase in the application of quantum mechanics (QM) methods to describe properties related to the ADMET profile of small molecules. The application of these methods allows calculating useful descriptors and physiochemical properties contributing to ADMET prediction. Considering that QM methods are the only one that describe the electronic state of a molecules, such methods are particularly useful for studying the metabolism of drugs; furthermore, the introduction of mixed QM and molecular mechanics (QM/MM) is also increasing the understanding of drug interaction with cytochromes from a mechanistic point of view. Finally, combining the increase number of experimental data with machine learning algorithms and QM-derived descriptors allowed the creation of an end-user software capable of affecting the drug discovery process.
近年来，量子力学（QM）方法在描述与小分子 ADMET 特征相关的性质方面的应用有所增加。这些方法的应用可以计算出有助于 ADMET 预测的有用描述符和物理化学性质。考虑到 QM 方法是唯一能够描述分子电子状态的方法，因此这些方法特别有助于研究药物的代谢；此外，混合 QM 和分子力学（QM/MM）的引入也从机制角度增加了对药物与细胞色素相互作用的理解。最后，将越来越多的实验数据与机器学习算法和 QM 衍生的描述符相结合，创建了一个能够影响药物发现过程的终端用户软件。
