Prediction of Off-Target Drug Effects Through Data Fusion
Prediction of Off-Target Drug Effects Through Data Fusion
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DOI：
10.1142/9789814583220_0016
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发表时间：
2013-11
影响因子：
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通讯作者：
Emmanuel R. Yera;A. Cleves;Ajay N. Jain
中科院分区：
文献类型：
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作者：
Emmanuel R. Yera;A. Cleves;Ajay N. Jain
我们提出了一个概率数据融合框架，它结合了多种计算方法来绘制药物和靶标之间的关系。这种方法对于识别令人惊讶的非预期药物生物靶标具有特别的相关性。分子之间的比较是基于二维拓扑结构的考虑，基于三维表面特征，并基于临床效果的英文描述。每个通道内的相似性计算被转换为概率分数。给定一个新分子以及一组共享某些生物效应的分子，基于与已知集合的比较产生单个分数，反映2D相似性、3D相似性、临床效果相似性或它们的组合。这些方法在成熟的结构药理学数据库(SPDB)中得到验证，并通过对来自CHEMBL数据库的数据的盲法应用进行了进一步的测试。对于偏离目标的效果的预测，3D-相似性作为一种单一的模式表现最好，但结合所有方法产生了性能提升。给出了结构上令人惊讶的偏离目标预测的引人注目的例子。
We present a probabilistic data fusion framework that combines multiple computational approaches for drawing relationships between drugs and targets. The approach has special relevance to identifying surprising unintended biological targets of drugs. Comparisons between molecules are made based on 2D topological structural considerations, based on 3D surface characteristics, and based on English descriptions of clinical effects. Similarity computations within each modality were transformed into probability scores. Given a new molecule along with a set of molecules sharing some biological effect, a single score based on comparison to the known set is produced, reflecting either 2D similarity, 3D similarity, clinical effects similarity or their combination. The methods were validated within acurated structural pharmacology database (SPDB) and further tested by blind application to data derived from the ChEMBL database. For prediction of off-target effects, 3D-similarity performed best as a single modality, but combining all methods produced performance gains. Striking examples of structurally surprising off-target predictions are presented.