Chen Jonathan Jun Feng, Visco Donald P
Department of Biology, The University of Akron, 302 Buchtel Common, Akron, OH 44325, USA.
Department of Chemical and Biomolecular Engineering, The University of Akron, 302 Buchtel Common, Akron, OH 44325, USA.
Eur J Med Chem. 2017 Nov 10;140:31-41. doi: 10.1016/j.ejmech.2017.08.056. Epub 2017 Sep 1.
There currently is renewed interest in blood clotting Factor XII as a potential target for thrombosis inhibition. Historically untargeted, there is little drug information with which to start drug candidate searches. Typical high-throughput screening can identify potential drug candidates, but is inefficient. Virtual high-throughput screening can be used to raise efficiency by focusing experimental efforts on compounds predicted to be active and is applied here to identify new Factor XIIa inhibitors. We combine principal component analysis, genetic algorithm and support vector machine to create the models used in the virtual high-throughput screening. In this work, experimental data from a PubChem Bioassay was used to train predictive models of Factor XIIa inhibition activity. The models created were then used to virtually screen the entire 72 million PubChem Compound database. Experimental validation of select candidates identified by this process resulted in a 42.9% hit-rate in the first-pass and 100% hit-rate in the second-pass, suggesting the effectiveness of the approach.
目前，人们对凝血因子XII作为血栓形成抑制的潜在靶点重新产生了兴趣。从历史上看，该靶点未被关注，几乎没有可供开展候选药物搜索的药物信息。典型的高通量筛选能够识别潜在的候选药物，但效率低下。虚拟高通量筛选可通过将实验工作集中于预测具有活性的化合物来提高效率，本文将其用于识别新型XIIa因子抑制剂。我们结合主成分分析、遗传算法和支持向量机来创建虚拟高通量筛选中使用的模型。在这项工作中，来自PubChem生物测定的实验数据被用于训练XIIa因子抑制活性的预测模型。然后，所创建的模型被用于对整个7200万的PubChem化合物数据库进行虚拟筛选。对通过该过程鉴定出的选定候选物进行实验验证，首次筛选的命中率为42.9%，二次筛选的命中率为100%，表明该方法有效。
