.png){kind=icon}
返回
NLP-inspired structural pattern recognition in chemical application
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2014.02.012.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper we report on a new structural pattern recognition approach for in silico prediction of chemical activity. It is based on grammatical inference on strings representing chemical compounds and string edit distance between a chemical compound and a formal grammar generalizing an activity class. In the late 1980s Weininger published a chemical language with a very simple and natural grammar. Recently, the algorithms suitable to process this language have been developed. From modeling of chemical activity with formal grammars and chemical compounds as words, a functionality is derivable to search for structural alerts, that is, molecular substructures and their combinatorial patterns that cause a molecule to have properties of interest. A biodegradability prediction system has been constructed to serve as an example throughout the paper. The source code and various files from the experiment are available from the corresponding author on request. (C) 2014 Elsevier B.V. All rights reserved.
Keyword:
Structural pattern recognition
Grammar inference
Natural language processing
Chemical descriptors
SMILES
Activity prediction
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.3
论文数:
8.0K
被引数:
1.6W
机构
暂无机构信息
引用论文
SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules微笑，一种化学语言和信息系统。1.介绍方法和编码规则
PREAI
分享收藏分享收藏
Graph kernels for chemical compounds using topological and three-dimensional local atom pair environments.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
查看更多内容