Conformer Generation for Structure-Based Drug Design: How Many and How Good?
Conformer Generation for Structure-Based Drug Design: How Many and How Good?
复制标题
DOI：
10.1021/acs.jcim.3c01245
复制
发表时间：
2023-11-13
影响因子：
5.6
通讯作者：
Koes, David Ryan
中科院分区：
文献类型：
--
作者：
McNutt, Andrew T.;Bisiriyu, Fatimah;Song, Sophia;Vyas, Ananya;Hutchison, Geoffrey R.;Koes, David Ryan
构象生成，即为小分子分配真实的三维坐标，是基于结构的药物设计的基础。刚体匹配算法需要构象集成，例如基于形状的方法或药效团方法，甚至灵活处理配体的方法，例如对接，都依赖于所提供构象的质量，因为没有采样所有自由度（例如，仅采样扭矩）。在这里，我们从经验上阐明了在常见的基于结构的药物发现任务中获得最佳性能所需的构象集合的大小、多样性和质量的一些一般原则。在许多情况下，我们的发现可能与该领域从业者所熟知的“常识”相似。尽管如此，我们认为量化这些构象效应是有价值的，同时在先前的研究基础上进行再现和扩展。具体而言，我们研究了最先进的生成式深度学习方法与更经典的基于几何的方法的性能，作为后处理步骤的能量最小化的影响，集合大小（最大数量的构象）和结构（通过多样性的均方根偏差过滤）的影响，以及这些选择如何影响概括生物活性构象和执行药效团筛选和分子对接的能力。
Conformer generation, the assignment of realistic 3D coordinates to a small molecule, is fundamental to structure-based drug design. Conformational ensembles are required for rigid-body matching algorithms, such as shape-based or pharmacophore approaches, and even methods that treat the ligand flexibly, such as docking, are dependent on the quality of the provided conformations due to not sampling all degrees of freedom (e.g., only sampling torsions). Here, we empirically elucidate some general principles about the size, diversity, and quality of the conformational ensembles needed to get the best performance in common structure-based drug discovery tasks. In many cases, our findings may parallel “common knowledge” well-known to practitioners of the field. Nonetheless, we feel that it is valuable to quantify these conformational effects while reproducing and expanding upon previous studies. Specifically, we investigate the performance of a state-of-the-art generative deep learning approach versus a more classical geometry-based approach, the effect of energy minimization as a postprocessing step, the effect of ensemble size (maximum number of conformers), and construction (filtering by root-mean-square deviation for diversity) and how these choices influence the ability to recapitulate bioactive conformations and perform pharmacophore screening and molecular docking.
登录
查看更多内容
影响因子：
5.6
作者：
Friedrich, Nils-Ole;Meyder, Agnes;Kirchmair, Johannes
通讯作者：
Kirchmair, Johannes
影响因子：
4.1
作者：
CRIPPEN, GM
通讯作者：
CRIPPEN, GM
影响因子：
5.6
作者：
Koes DR;Camacho CJ
通讯作者：
Camacho CJ
影响因子：
7.3
作者：
Mysinger, Michael M.;Carchia, Michael;Irwin, John. J.;Shoichet, Brian K.
通讯作者：
Shoichet, Brian K.
影响因子：
3.7
作者：
Chen, Lieyang;Cruz, Anthony;Kurtzman, Tom
通讯作者：
Kurtzman, Tom