Damm-Ganamet Kelly L, Bembenek Scott D, Venable Jennifer W, Castro Glenda G, Mangelschots Lieve, Peeters Daniëlle C G, Mcallister Heather M, Edwards James P, Disepio Daniel, Mirzadegan Taraneh
Discovery Sciences and ‡Immunology, Janssen Research & Development , San Diego, California 92121, United States.
J Med Chem. 2016 May 12;59(9):4302-13. doi: 10.1021/acs.jmedchem.5b01974. Epub 2016 Apr 28.
Here, we report a high-throughput virtual screening (HTVS) study using phosphoinositide 3-kinase (both PI3Kγ and PI3Kδ). Our initial HTVS results of the Janssen corporate database identified small focused libraries with hit rates at 50% inhibition showing a 50-fold increase over those from a HTS (high-throughput screen). Further, applying constraints based on "chemically intuitive" hydrogen bonds and/or positional requirements resulted in a substantial improvement in the hit rates (versus no constraints) and reduced docking time. While we find that docking scoring functions are not capable of providing a reliable relative ranking of a set of compounds, a prioritization of groups of compounds (e.g., low, medium, and high) does emerge, which allows for the chemistry efforts to be quickly focused on the most viable candidates. Thus, this illustrates that it is not always necessary to have a high correlation between a computational score and the experimental data to impact the drug discovery process.
在此，我们报告了一项使用磷酸肌醇3激酶（PI3Kγ和PI3Kδ）的高通量虚拟筛选（HTVS）研究。我们对杨森公司数据库进行的初始HTVS结果确定了小型聚焦文库，其50%抑制率的命中率比高通量筛选（HTS）的命中率提高了50倍。此外，基于“化学直观”氢键和/或位置要求施加约束，导致命中率大幅提高（与无约束相比）并减少了对接时间。虽然我们发现对接评分函数无法提供一组化合物的可靠相对排名，但确实出现了化合物组的优先级划分（例如低、中、高），这使得化学研究工作能够迅速集中在最可行的候选物上。因此，这表明在影响药物发现过程时，计算得分与实验数据之间并不总是需要有高度相关性。
