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Logistic Regression Method for Ligand Discovery.
Journal of Computational Biology ( IF 1.7 ) Pub Date : 2020-06-05 , DOI: 10.1089/cmb.2019.0232
Chian Chen,Hsiuying Wang

Protein-based virtual screening is integral to the modern drug discovery process. Most protein-based virtual screening experiments are performed using docking programs. The accuracy of a docking program strongly relies on the incorporated scoring function used, which is based on various energy terms. The existing scoring functions deal with the energy terms that use the equal weight function or other weight functions, which do not depend on characteristics of the protein. To improve the existing methods, Lu and Wang proposed a protein-specific scoring function based on a regression analysis that was shown to have higher performance than the existing methods. In this study, we propose a protein-specific scoring approach to select potential ligands based on logistic regression analysis. The performance of our method was evaluated using the Directory of Useful Decoys docked data set, which contains 40 protein targets. The results showed that the proposed method can increase the enrichment factors for most of the 40 protein targets.

中文翻译:

配体发现的逻辑回归方法。

基于蛋白质的虚拟筛选是现代药物发现过程中不可或缺的一部分。大多数基于蛋白质的虚拟筛选实验是使用对接程序进行的。对接程序的准确性在很大程度上依赖于所使用的综合评分函数,该函数基于各种能量项。现有的评分函数处理能量项,使用等权函数或其他权函数,不依赖于蛋白质的特性。为了改进现有方法,Lu 和 Wang 提出了一种基于回归分析的蛋白质特异性评分函数,该函数被证明比现有方法具有更高的性能。在这项研究中,我们提出了一种基于逻辑回归分析的蛋白质特异性评分方法来选择潜在的配体。使用包含 40 个蛋白质目标的有用诱饵停靠数据集目录评估了我们方法的性能。结果表明,所提出的方法可以增加40个蛋白质靶标中的大多数的富集因子。
更新日期:2020-06-05
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