Predicting binding affinity of CSAR ligands using both structure-based and ligand-based approaches.
Predicting binding affinity of CSAR ligands using both structure-based and ligand-based approaches.
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DOI：
10.1021/ci400216q
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发表时间：
2013-08-26
影响因子：
5.6
通讯作者：
Tropsha A
中科院分区：
文献类型：
--
作者：
Fourches D;Muratov E;Ding F;Dokholyan NV;Tropsha A
我们报告的预测准确性的配体为基础的（二维QSAR）和结构为基础的（MedusaDock）的方法，独立使用和共识排名同源系列的配体结合三个蛋白质靶点（英国，ERK 2和CHK 1）从CSAR 2011基准演习。预测QSAR模型的合奏开发使用已知的绑定器，这三个目标提取的公开可用的ChEMBL数据库。使用选定的模型预测CSAR化合物对相应靶标的结合亲和力，并对其进行相应的排名;通过斯皮尔曼相关性评估的总体排名准确度对于UK高达0.78，对于ERK 2为0.60，对于CHK 1为0.56，将我们的预测置于所有参与者中的前10%。同时，MedusaDock设计用于预测可靠的对接姿势，也用于根据它们的对接评分对CSAR配体进行排名; UK、ERK 2和CHK 1的准确度（斯皮尔曼相关性）分别为0.76、0.31和0.26。此外，已经探索了结合MedusaDock和QSAR预测等级的几种共识方法的性能;最好的方法产生了UK，ERK 2和CHK 1的斯皮尔曼相关系数分别为0.82，0.50和0.45。这项研究表明，（i）外部验证的2D QSAR模型能够对CSAR配体进行排序，至少与我们和其他小组使用的基于结构的计算密集型方法一样准确，（ii）基于配体的QSAR模型可以通过提高预测性能来补充基于结构的方法。
We report on the prediction accuracy of ligand-based (2D QSAR) and structure-based (MedusaDock) methods used both independently and in consensus for ranking the congeneric series of ligands binding to three protein targets (UK, ERK2, and CHK1) from the CSAR 2011 benchmark exercise. An ensemble of predictive QSAR models was developed using known binders of these three targets extracted from the publicly-available ChEMBL database. Selected models were used to predict the binding affinity of CSAR compounds towards the corresponding targets and rank them accordingly; the overall ranking accuracy evaluated by Spearman correlation was as high as 0.78 for UK, 0.60 for ERK2, and 0.56 for CHK1, placing our predictions in top-10% among all the participants. In parallel, MedusaDock designed to predict reliable docking poses was also used for ranking the CSAR ligands according to their docking scores; the resulting accuracy (Spearman correlation) for UK, ERK2, and CHK1 were 0.76, 0.31, and 0.26, respectively. In addition, performance of several consensus approaches combining MedusaDock and QSAR predicted ranks altogether has been explored; the best approach yielded Spearman correlation coefficients for UK, ERK2, and CHK1 of 0.82, 0.50, and 0.45, respectively. This study shows that (i) externally validated 2D QSAR models were capable of ranking CSAR ligands at least as accurately as more computationally intensive structure-based approaches used both by us and by other groups and (ii) ligand-based QSAR models can complement structure-based approaches by boosting the prediction performances when used in consensus.
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影响因子：
5.6
作者：
Damm-Ganamet KL;Smith RD;Dunbar JB Jr;Stuckey JA;Carlson HA
通讯作者：
Carlson HA
影响因子：
5.6
作者：
Hsieh JH;Yin S;Liu S;Sedykh A;Dokholyan NV;Tropsha A
通讯作者：
Tropsha A
影响因子：
--
作者：
Cherkassky, V
通讯作者：
Cherkassky, V
影响因子：
5.6
作者：
Fourches D;Muratov E;Tropsha A
通讯作者：
Tropsha A
DOI：
10.1023/b:jcam.0000017496.76572.6f
发表时间：
2003-11-01
影响因子：
3.5
作者：
Bursulaya, BD;Totrov, M;Brooks, CL
通讯作者：
Brooks, CL