D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies
D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies
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DOI：
10.1007/s10822-020-00289-y
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发表时间：
2020-02-01
影响因子：
3.5
通讯作者：
Gilson, Michael K.
中科院分区：
文献类型：
--
作者：
Parks, Conor D.;Gaieb, Zied;Gilson, Michael K.
药物设计数据资源（D3R）旨在通过盲配体姿态预测和亲和力挑战确定计算机辅助药物设计的最佳实践方法。在此，我们报告了大挑战4 （GC4）的结果。GC4专注于蛋白β分泌酶1和组织蛋白酶S，并以类似于先前挑战的方式运行。在第一阶段，评估参与者预测BACE1配体位姿和亲和力的能力。在第一阶段完成后，释放所有BACE1共晶结构，第二阶段测试与共晶结构的亲和度排名。我们提供了对结果的分析，并讨论了确定的最佳实践方法的见解。
The Drug Design Data Resource (D3R) aims to identify best practice methods for computer aided drug design through blinded ligand pose prediction and affinity challenges. Herein, we report on the results of Grand Challenge 4 (GC4). GC4 focused on proteins beta secretase 1 and Cathepsin S, and was run in an analogous manner to prior challenges. In Stage 1, participant ability to predict the pose and affinity of BACE1 ligands were assessed. Following the completion of Stage 1, all BACE1 co-crystal structures were released, and Stage 2 tested affinity rankings with co-crystal structures. We provide an analysis of the results and discuss insights into determined best practice methods.