EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh, EH9 3FJ, UK.
J Comput Aided Mol Des. 2018 Jan;32(1):199-210. doi: 10.1007/s10822-017-0083-9. Epub 2017 Nov 13.
The Drug Design Data Resource (D3R) consortium organises blinded challenges to address the latest advances in computational methods for ligand pose prediction, affinity ranking, and free energy calculations. Within the context of the second D3R Grand Challenge several blinded binding free energies predictions were made for two congeneric series of Farsenoid X Receptor (FXR) inhibitors with a semi-automated alchemical free energy calculation workflow featuring FESetup and SOMD software tools. Reasonable performance was observed in retrospective analyses of literature datasets. Nevertheless, blinded predictions on the full D3R datasets were poor due to difficulties encountered with the ranking of compounds that vary in their net-charge. Performance increased for predictions that were restricted to subsets of compounds carrying the same net-charge. Disclosure of X-ray crystallography derived binding modes maintained or improved the correlation with experiment in a subsequent rounds of predictions. The best performing protocols on D3R set1 and set2 were comparable or superior to predictions made on the basis of analysis of literature structure activity relationships (SAR)s only, and comparable or slightly inferior, to the best submissions from other groups.
药物设计数据资源（D3R）联盟组织了盲测挑战，以解决配体构象预测、亲和力排序和自由能计算方面的最新计算方法进展。在第二届 D3R 大挑战中，使用半自动的自由能计算工作流程（包含 FESetup 和 SOMD 软件工具）对两种同源的法尼醇 X 受体（FXR）抑制剂系列进行了若干盲测结合自由能预测。在对文献数据集的回顾性分析中观察到了合理的性能。然而，由于对净电荷不同的化合物的排序遇到困难，对完整 D3R 数据集的盲测预测结果不佳。对于限制在具有相同净电荷的化合物子集上的预测，性能有所提高。在随后的预测轮次中，披露基于 X 射线晶体学的结合模式保持或提高了与实验的相关性。在 D3R set1 和 set2 上表现最好的方案与仅基于文献结构活性关系（SAR）分析的预测相当或更好，与其他组的最佳提交结果相当或略差。
