Automated Chemical Structure Extraction

自动从文献储存库(如pdf文档、期刊、图像)提取化学结构到机器可读的格式。这是为了从本来可以大量使用的人工资源中推断出丰富的数据，以处理化学工业中正在进行的未优化的过程。它还为利用图像识别将分子结构转换为不同格式这一目前尚未解决的可靠性问题提供了一种扩展解决方案的潜力。通过自动化这种未开发的资源；这为更多数据驱动的优化提供了机会，以帮助优化化学反应/机理步骤。建议的解决方案和方法使用图像识别来自动分割期刊文章和其他资源中的图像，以收集大量机器可读的分子实例。目前提出的方法是使用端到端的深度学习流水线来定义分割到转换的阶段。

Automating chemical structure extraction from literature repositories such as, pdf documents, journals, images, into machine a machine-readable format. This is designed to extrapolate a wealth of data from an otherwise manual resource that can be used in large quantities to tackle ongoing unoptimized processes within the chemical industry. It also presents the potential for an expansive solution to the currently unsolved reliability problem of converting molecular structures to different formats using Image Recognition. By automating such an untapped resource; this opens opportunities for more data driven optimisation to help in the need for optimising chemical reaction/mechanism steps.Proposed solution and methodologyUse image recognition to autonomously segment images from journal articles and other resources to collect a wealth of machine-readable instances of molecules. The current proposed methodology is to use an end to end deep learning pipeline to define the stages of segmentation to conversion.