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ChemFLuo:一个用于荧光化合物结构分析和鉴定的网络服务器。

ChemFLuo: a web-server for structure analysis and identification of fluorescent compounds.

机构信息

Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410003, P. R. China.

Central South University of Forestry and Technology, Changsha, 410004, P.R. China.

出版信息

Brief Bioinform. 2021 Jul 20;22(4). doi: 10.1093/bib/bbaa282.

Abstract

BACKGROUND

Fluorescent detection methods are indispensable tools for chemical biology. However, the frequent appearance of potential fluorescent compound has greatly interfered with the recognition of compounds with genuine activity. Such fluorescence interference is especially difficult to identify as it is reproducible and possesses concentration-dependent characteristic. Therefore, the development of a credible screening tool to detect fluorescent compounds from chemical libraries is urgently needed in early stages of drug discovery.

RESULTS

In this study, we developed a webserver ChemFLuo for fluorescent compound detection, based on two large and high-quality training datasets containing 4906 blue and 8632 green fluorescent compounds. These molecules were used to construct a group of prediction models based on the combination of three machine learning algorithms and seven types of molecular representations. The best blue fluorescence prediction model achieved with balanced accuracy (BA) = 0.858 and area under the receiver operating characteristic curve (AUC) = 0.931 for the validation set, and BA = 0.823 and AUC = 0.903 for the test set. The best green fluorescence prediction model achieved the prediction accuracy with BA = 0.810 and AUC = 0.887 for the validation set, and BA = 0.771 and AUC = 0.852 for the test set. Besides prediction model, 22 blue and 16 green representative fluorescent substructures were summarized for the screening of potential fluorescent compounds. The comparison with other fluorescence detection tools and theapplication to external validation sets and large molecule libraries have demonstrated the reliability of prediction model for fluorescent compound detection.

CONCLUSION

ChemFLuo is a public webserver to filter out compounds with undesirable fluorescent properties, which will benefit the design of high-quality chemical libraries for drug discovery. It is freely available at http://admet.scbdd.com/chemfluo/index/.

摘要

背景

荧光检测方法是化学生物学不可或缺的工具。然而,潜在荧光化合物的频繁出现极大地干扰了对具有真正活性的化合物的识别。这种荧光干扰尤其难以识别,因为它具有重现性和浓度依赖性特征。因此,在药物发现的早期阶段,迫切需要开发一种可靠的筛选工具来检测化学文库中的荧光化合物。

结果

在这项研究中,我们基于包含 4906 个蓝色和 8632 个绿色荧光化合物的两个大型高质量训练数据集,开发了一个名为 ChemFLuo 的荧光化合物检测网络服务器。这些分子用于构建一组基于三种机器学习算法和七种分子表示类型组合的预测模型。对于验证集,最佳蓝色荧光预测模型的平衡准确性(BA)= 0.858,接收者操作特征曲线下的面积(AUC)= 0.931,对于测试集,BA = 0.823 和 AUC = 0.903。最佳绿色荧光预测模型的预测准确性为验证集的 BA = 0.810 和 AUC = 0.887,测试集的 BA = 0.771 和 AUC = 0.852。除了预测模型外,还总结了 22 个蓝色和 16 个绿色代表性荧光亚结构,用于筛选潜在的荧光化合物。与其他荧光检测工具的比较以及对外部验证集和大型分子库的应用表明,预测模型对于荧光化合物检测具有可靠性。

结论

ChemFLuo 是一种公共网络服务器,用于过滤掉具有不理想荧光性质的化合物,这将有益于高质量化学文库的设计,以用于药物发现。它可在 http://admet.scbdd.com/chemfluo/index/ 免费获取。

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