Quantitative surface field analysis: learning causal models to predict ligand binding affinity and pose.
Quantitative surface field analysis: learning causal models to predict ligand binding affinity and pose.
复制标题
DOI：
10.1007/s10822-018-0126-x
复制
发表时间：
2018-07
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
我们介绍了仅基于结构-活性数据的QuanSA方法来诱导物理上有意义的基于场的配体结合口袋模型。该方法与QMOD方法密切相关，将学习到的评分域替换为由分子片段构建的口袋。一般地解决了配体相互对齐的问题，并通过多实例机器学习识别出最优的模型参数和配体位姿。我们提供了16个结构-活性数据集的算法细节以及性能结果，涵盖了许多药学相关的目标。特别是，我们展示了最初从小数据集诱导的模型如何能够推断出具有非常高特异性的新型基础支架的有效新配体。此外，我们表明，将QuanSA模型的预测与基于物理的模拟方法的预测相结合是协同的。QuanSA预测产生结合亲和力，明确估计配体应变，相关配体姿势家族，以及估计结构新颖性和置信度。该方法适用于细粒度引线优化和有效的新引线识别。
We introduce the QuanSA method for inducing physically meaningful field-based models of ligand binding pockets based on structure-activity data alone. The method is closely related to the QMOD approach, substituting a learned scoring field for a pocket constructed of molecular fragments. The problem of mutual ligand alignment is addressed in a general way, and optimal model parameters and ligand poses are identified through multiple-instance machine learning. We provide algorithmic details along with performance results on sixteen structure-activity data sets covering many pharmaceutically relevant targets. In particular, we show how models initially induced from small data sets can extrapolatively identify potent new ligands with novel underlying scaffolds with very high specificity. Further, we show that combining predictions from QuanSA models with those from physics-based simulation approaches is synergistic. QuanSA predictions yield binding affinities, explicit estimates of ligand strain, associated ligand pose families, and estimates of structural novelty and confidence. The method is applicable for fine-grained lead optimization as well as potent new lead identification.
登录
查看更多内容
影响因子：
3.5
作者：
Cleves AE;Jain AN
通讯作者：
Jain AN
影响因子：
3.5
作者：
Jain AN;Cleves AE
通讯作者：
Cleves AE
影响因子：
7.3
作者：
Cramer, RD
通讯作者：
Cramer, RD
影响因子：
6.3
作者：
Genheden S;Ryde U
通讯作者：
Ryde U
影响因子：
5.6
作者：
Baum, Bernhard;Muley, Laveena;Klebe, Gerhard
通讯作者：
Klebe, Gerhard