Optimization of molecular docking scores with support vector rank regression
Optimization of molecular docking scores with support vector rank regression
复制标题
使用支持向量秩回归优化分子对接分数
DOI：
10.1002/prot.24282
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发表时间：
2013-08
期刊：
影响因子：
--
通讯作者：
陈新
中科院分区：
文献类型：
--
作者：
陈新
本文介绍了基于支持向量秩回归（SVRR）的分子对接分数优化算法。采用SVRR算法对两个对接软件报告的7个原始对接分数进行整合。结果SVRR分数显示，结合构象预测测试平均提高了12.1%(59.5-66.7%)，将正确计算的构象排在第一位，同时排名靠前的构象的RMSD提高了16.7% （2.5414 vs. 2.1162 Å）。在化合物文库筛选（LS）试验中，平均提高46.3%(18.2-26.6%)，将正确的配体排在首位。此外，研究表明，使用不同训练策略的不同样本数据集训练的SVRR分数都表现出非常一致的准确性，这表明SVRR算法具有很强的鲁棒性和泛化性。相比之下，在相同的训练数据集上，传统的支持向量分类和回归算法无法相对提高LS和构象预测的准确性。这些结果表明，SVRR算法具有额外的特征来表明计算的结合构象之间的比较适合度，有可能创建一个更准确的综合对接评分的新类别。蛋白质2013;81:1386 - 1398。©2013 Wiley期刊公司
This work introduces the support vector rank regression (SVRR) algorithm for the optimization of molecular docking scores. Seven original docking scores reported by two docking software were integrated by the SVRR algorithm. The resulting SVRR scores showed an average of 12.1% improvement (59.5–66.7%) in binding conformation prediction tests to rank the correctly computed conformation in the first place, along with 16.7% RMSD improvement (2.5414 vs. 2.1162 Å) for the top ranked conformations. In compound library screening (LS) tests, an average of 46.3% improvement (18.2–26.6%) was also observed to rank the correct ligand in the first place. Furthermore, it was shown that SVRR scores trained with different example datasets, using different training strategies, all exhibited exceedingly consistent accuracies, suggesting that the SVRR algorithm is highly robust and generalizable. In contrast, using the same training datasets, traditional support vector classification and regression algorithms failed to improve comparably the accuracy of LS and conformation prediction. These results suggested that, with additional features to indicate the comparative fitness between computed binding conformations, the SVRR algorithm holds the potential to create a new category of more accurate integrative docking scores. Proteins 2013; 81:1386–1398. © 2013 Wiley Periodicals, Inc.
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