Institute of Single Cell Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China.
School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China.
Molecules. 2020 Mar 18;25(6):1375. doi: 10.3390/molecules25061375.
Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure- and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.
药物研发是制药行业最重要的过程之一。各种计算方法极大地缩短了药物发现的时间和成本。在这篇综述中，我们首先讨论了多尺度生物分子模拟在识别靶大分子上的药物结合位点和阐明药物作用机制方面的作用。然后，介绍并讨论了虚拟筛选方法（例如分子对接、药效基团建模和 QSAR）以及基于结构和基于配体的经典/从头药物设计。最后，我们探讨了机器学习方法的发展及其在上述计算方法中的应用，以加速药物发现过程。此外，还讨论了结合多种方法的几个应用实例。将不同方法结合起来，共同解决不同尺度和维度的难题，将是药物筛选和设计的必然趋势。
