从 2011 年 CSAR 基准测试中使用 smina 进行经验评分中获得的经验教训。
Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise.
机构信息
Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA.
出版信息
J Chem Inf Model. 2013 Aug 26;53(8):1893-904. doi: 10.1021/ci300604z. Epub 2013 Feb 12.
Abstract
We describe a general methodology for designing an empirical scoring function and provide smina, a version of AutoDock Vina specially optimized to support high-throughput scoring and user-specified custom scoring functions. Using our general method, the unique capabilities of smina, a set of default interaction terms from AutoDock Vina, and the CSAR (Community Structure-Activity Resource) 2010 data set, we created a custom scoring function and evaluated it in the context of the CSAR 2011 benchmarking exercise. We find that our custom scoring function does a better job sampling low RMSD poses when crossdocking compared to the default AutoDock Vina scoring function. The design and application of our method and scoring function reveal several insights into possible improvements and the remaining challenges when scoring and ranking putative ligands.
摘要
我们描述了一种设计经验评分函数的通用方法，并提供了 smina，这是特别优化以支持高通量评分和用户指定的自定义评分功能的 AutoDock Vina 的一个版本。使用我们的通用方法、smina 的独特功能、AutoDock Vina 的一组默认相互作用项以及 CSAR（社区结构-活性资源）2010 数据集，我们创建了一个自定义评分函数，并在 CSAR 2011 基准测试中对其进行了评估。我们发现，与默认的 AutoDock Vina 评分函数相比，我们的自定义评分函数在对接时更能更好地采样低 RMSD 构象。我们的方法和评分函数的设计和应用揭示了在评分和排列可能的配体时，改进和剩余挑战的一些见解。
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