Extrapolative prediction using physically-based QSAR.
Extrapolative prediction using physically-based QSAR.
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DOI：
10.1007/s10822-016-9896-1
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发表时间：
2016-02
影响因子：
3.5
通讯作者：
Jain AN
中科院分区：
文献类型：
--
作者：
Cleves AE;Jain AN
Surflex-QMOD集成了化学结构和活性数据，以产生用于结合亲和力预测的物理现实模型。在这里，我们将QMOD应用于3D-QSAR基准数据集，并显示出对各种目标的广泛适用性。在QMOD模型中测试新配体采用自动灵活的分子比对，模型本身定义每个配体的最佳姿势。QMOD的性能进行了比较，四种方法，依赖于手动对齐（CoMFA，两种变化的CoMSIA，和CMF）。QMOD在具有挑战性但结构有限的测试集上显示出与其他方法相当的性能。QMOD模型还被应用于测试来自ChEMBL的配体的大型且结构多样的数据集，几乎所有这些数据集都是在用于模型构建的那些之后数年合成的。不同的化学结构之间的外推是可能的，因为该方法解决了配位体的问题，并提供了结构和几何手段，以定量识别配位体模型的适用性域。基于等级相关性，对四种测试靶标的此类配体的预测具有高度统计学显著性。预测为高活性的那些分子（）的平均实验值为7.5，通过QMOD为每个靶点鉴定了有效的和结构新颖的配体。
Surflex-QMOD integrates chemical structure and activity data to produce physically-realistic models for binding affinity prediction . Here, we apply QMOD to a 3D-QSAR benchmark dataset and show broad applicability to a diverse set of targets. Testing new ligands within the QMOD model employs automated flexible molecular alignment, with the model itself defining the optimal pose for each ligand. QMOD performance was compared to that of four approaches that depended on manual alignments (CoMFA, two variations of CoMSIA, and CMF). QMOD showed comparable performance to the other methods on a challenging, but structurally limited, test set. The QMOD models were also applied to test a large and structurally diverse dataset of ligands from ChEMBL, nearly all of which were synthesized years after those used for model construction. Extrapolation across diverse chemical structures was possible because the method addresses the ligand pose problem and provides structural and geometric means to quantitatively identify ligands within a model’s applicability domain. Predictions for such ligands for the four tested targets were highly statistically significant based on rank correlation. Those molecules predicted to be highly active () had a mean experimental of 7.5, with potent and structurally novel ligands being identified by QMOD for each target.