Rastelli Giulio, Pinzi Luca
Department of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy.
Front Chem. 2019 Jul 11;7:498. doi: 10.3389/fchem.2019.00498. eCollection 2019.
High-throughput docking is an established computational screening approach in drug design. This methodology enables a rapid identification of biologically active hit compounds, providing an efficient and cost-effective complement or alternative to experimental high-throughput screenings. However, limitations inherent to the methodology make docking results inevitably approximate. Two major Achille's heels include the use of approximated scoring functions and the limited sampling of the ligand-target complexes. Therefore, docking results require careful evaluation and further post-docking analyses. In this article, we will overview our approach to post-docking analysis in virtual screenings. BEAR (Binding Estimation After Refinement) was developed as a post-docking processing tool that refines docking poses by means of molecular dynamics (MD) and then rescores the ligands based on more accurate scoring functions (MM-PB(GB)SA). The tool has been validated and used prospectively in drug discovery applications. Future directions regarding refinement and rescoring in virtual screening are discussed.
高通量对接是药物设计中一种既定的计算筛选方法。该方法能够快速识别具有生物活性的命中化合物，为实验性高通量筛选提供一种高效且经济高效的补充或替代方法。然而，该方法固有的局限性使得对接结果不可避免地存在近似性。两个主要的薄弱环节包括使用近似的评分函数以及对配体 - 靶点复合物的有限采样。因此，对接结果需要仔细评估和进一步的对接后分析。在本文中，我们将概述虚拟筛选中对接后分析的方法。BEAR（精炼后结合估计）作为一种对接后处理工具而开发，它通过分子动力学（MD）优化对接构象，然后基于更精确的评分函数（MM-PB(GB)SA）对配体重新评分。该工具已在药物发现应用中得到验证并被前瞻性地使用。还讨论了虚拟筛选中关于优化和重新评分的未来方向。
