Liu Zhihong, Du Jiewen, Lin Ziying, Li Ze, Liu Bingdong, Cui Zongbin, Fang Jiansong, Xie Liwei
School of Public Health, Xinxiang Medical University, Xinxiang, China.
Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China.
Comput Struct Biotechnol J. 2022 Aug 2;20:4082-4097. doi: 10.1016/j.csbj.2022.07.045. eCollection 2022.
Various deep learning-based architectures for molecular generation have been proposed for drug design. The flourish of the molecular generation methods and applications has created a great demand for the visualization and functional profiling for the generated molecules. An increasing number of publicly available chemogenomic databases sets good foundations and creates good opportunities for comprehensive profiling of the de novo library. In this paper, we present DenovoProfiling, a webserver dedicated to library visualization and functional profiling. Currently, DenovoProfiling contains six modules: (1) identification & visualization module for chemical structure visualization and identify the reported structures, (2) chemical space module for chemical space exploration using similarity maps, principal components analysis (PCA), drug-like properties distribution, and scaffold-based clustering, (3) ADMET prediction module for predicting the ADMET properties of the molecules, (4) molecular alignment module for three dimensional molecular shape analysis, (5) drugs mapping module for identifying structural similar drugs, and (6) target & pathway module for identifying the reported targets and corresponding functional pathways. DenovoProfiling could provide structural identification, chemical space exploration, drug mapping, and target & pathway information. The comprehensive annotated information could give users a clear picture of their library and could guide the further selection of candidates for chemical synthesis and biological confirmation. DenovoProfiling is freely available at http://denovoprofiling.xielab.net.
为了药物设计，人们提出了各种基于深度学习的分子生成架构。分子生成方法和应用的蓬勃发展，对生成分子的可视化和功能分析产生了巨大需求。越来越多的公开化学基因组数据库为从头库的全面分析奠定了良好基础并创造了良好机遇。在本文中，我们介绍了DenovoProfiling，一个致力于库可视化和功能分析的网络服务器。目前，DenovoProfiling包含六个模块：（1）用于化学结构可视化和识别已报道结构的识别与可视化模块；（2）用于使用相似性图谱、主成分分析（PCA）、类药性质分布和基于骨架的聚类进行化学空间探索的化学空间模块；（3）用于预测分子ADMET性质的ADMET预测模块；（4）用于三维分子形状分析的分子比对模块；（5）用于识别结构相似药物的药物映射模块；（6）用于识别已报道靶点和相应功能途径的靶点与途径模块。DenovoProfiling可以提供结构识别、化学空间探索、药物映射以及靶点与途径信息。全面的注释信息可以让用户清楚了解他们的库，并可以指导进一步选择化学合成和生物学确认的候选物。DenovoProfiling可通过http://denovoprofiling.xielab.net免费获取。
