使用基于指纹的人工神经网络（FANN-QSAR）进行配体生物活性预测。
Ligand biological activity predictions using fingerprint-based artificial neural networks (FANN-QSAR).
作者信息
Myint Kyaw Z, Xie Xiang-Qun
机构信息
NIDA Center of Excellence for Computational Chemogenomics Drug Abuse Research, Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania, USA,
出版信息
Methods Mol Biol. 2015;1260:149-64. doi: 10.1007/978-1-4939-2239-0_9.
This chapter focuses on the fingerprint-based artificial neural networks QSAR (FANN-QSAR) approach to predict biological activities of structurally diverse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to train the FANN-QSAR models. The results were benchmarked against known 2D and 3D QSAR methods, and the derived models were used to predict cannabinoid (CB) ligand binding activities as a case study. In addition, the FANN-QSAR model was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds. We discovered several compounds with good CB2 binding affinities ranging from 6.70 nM to 3.75 μM. The studies proved that the FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research.
本章重点介绍基于指纹的人工神经网络定量构效关系（FANN-QSAR）方法，以预测结构多样的化合物的生物活性。使用三种类型的指纹，即ECFP6、FP2和MACCS，作为输入来训练FANN-QSAR模型。将结果与已知的二维和三维QSAR方法进行基准比较，并将所得模型用于预测大麻素（CB）配体结合活性作为案例研究。此外，FANN-QSAR模型用作虚拟筛选工具，在大型NCI化合物数据库中搜索潜在的大麻素先导化合物。我们发现了几种具有良好CB2结合亲和力的化合物，范围从6.70 nM到3.75 μM。研究证明，FANN-QSAR方法是预测配体生物活性或性质以及寻找药物发现研究新先导化合物的有用方法。
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