Ligand-Based Target Prediction with Signature Fingerprints
Ligand-Based Target Prediction with Signature Fingerprints
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DOI：
10.1021/ci500361u
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发表时间：
2014-10-01
影响因子：
5.6
通讯作者：
Noeske, Tobias
中科院分区：
文献类型：
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作者：
Alvarsson, Jonathan;Eklund, Martin;Noeske, Tobias
当评估潜在的候选药物时，期望在合成之前通过计算机模拟预测靶标相互作用，以便评估，例如，二级药理学这可以通过使用化学相似性搜索查看相似化合物的已知靶结合谱来完成。本研究的目的是构建和评价基于分子特征描述符的化学指纹图谱进行靶点结合预测的性能。为了进行比较，我们使用了受试者工作特征曲线下面积（AUC），并补充了净重新分类改善（NRI）。我们创建了两个开源签名指纹，一个位和一个计数版本，并评估了它们的性能相比，一组已建立的指纹方面的预测结合目标使用Tanimoto的相似性搜索公开可用的数据集提取的ChEMBL。结果表明，签名指纹的计数版本与ECFP等成熟指纹的性能相当。count版本的性能略优于bit版本;但是，count版本更复杂，需要更多的计算时间和内存来运行，因此其使用可能应该逐个评估。基于NRI的测试补充了基于AUC的测试，并显示出更高的功效。
When evaluating a potential drug candidate it is desirable to predict target interactions in silico prior to synthesis in order to assess, e.g., secondary pharmacology. This can be done by looking at known target binding profiles of similar compounds using chemical similarity searching. The purpose of this study was to construct and evaluate the performance of chemical fingerprints based on the molecular signature descriptor for performing target binding predictions. For the comparison we used the area under the receiver operating characteristics curve (AUC) complemented with net reclassification improvement (NRI). We created two open source signature fingerprints, a bit and a count version, and evaluated their performance compared to a set of established fingerprints with regards to predictions of binding targets using Tanimoto-based similarity searching on publicly available data sets extracted from ChEMBL. The results showed that the count version of the signature fingerprint performed on par with well-established fingerprints such as ECFP. The count version outperformed the bit version slightly; however, the count version is more complex and takes more computing time and memory to run so its usage should probably be evaluated on a case-by-case basis. The NRI based tests complemented the AUC based ones and showed signs of higher power.