Review of techniques and models used in optical chemical structure recognition in images and scanned documents

Schaub

et al. 2023

Preprint

Recent years have seen a sharp increase in the development of deep learning and artificial intelligence-based molecular informatics. There has been a growing interest in applying deep learning to several subfields, including the digital transformation of synthetic chemistry, extraction of chemical information from the scientific literature, and AI in natural product-based drug discovery. The application of AI to molecular informatics is still constrained by the fact that most of the data used for training and testing deep learning models are not available as FAIR and open data. As open science practices continue to grow in popularity, initiatives which support FAIR and open data, as well as open-source software, have emerged. It is becoming increasingly important for researchers in the field of molecular informatics to embrace open science and to submit data and software in open repositories. With the advent of open-source deep learning frameworks and cloud computing platforms, academic researchers are now able to deploy and test their own deep learning models with ease. With the development of new and faster hardware for deep learning and the increasing number of initiatives towards digital research data management infrastructures, as well as a culture promoting open data, open source, and open science, AI-driven molecular informatics will continue to grow. This review examines the current state of open data and open algorithms in molecular informatics, as well as ways in which they could be improved in future.

Section: Extraction Of Chemical Information From the Scientific Liter...mentioning

confidence: 99%

Section: Extraction Of Chemical Information From the Scientific Liter...mentioning

confidence: 99%

Open data and algorithms for open science in AI-driven molecular informatics

Schaub

et al. 2023

Preprint

“…The field of optical chemical structure recognition (OCSR) deals with the translation of images of chemical structures as they are published in the scientific literature into machine-readable representations of the underlying molecular graph [69,70]. In the past two years, a variety of deep learning-based OCSR methods [71][72][73][74] has been published, where DECIMER Image-Transformer [75], Img2Mol [76] and SwinOCSR [77] provide openly available source code and trained models.…”

Section: Extraction Of Chemical Information From the Scientific Liter...mentioning

confidence: 99%

“…Currently, DECIMER performs with an accuracy rate of above 90%. As an open system, DECIMER is regarded as an important point of reference for future work [70]. Without open databases like PubChem, where one can download over 100 million chemical structures for free, this wouldn't have been possible.…”

Section: Extraction Of Chemical Information From the Scientific Liter...mentioning

confidence: 99%

Open data and algorithms for open science in AI-driven molecular informatics

Schaub

et al. 2022

Preprint

Recent years have seen a sharp increase in the development of deep learning and artificial intelligence-based molecular informatics. The success of AlphaFold led to a growing interest in applying deep learning to a number of subfields, including the digital transformation of synthetic chemistry, extraction of chemical information from the scientific literature, and AI in natural product-based drug discovery. The application of AI to molecular informatics is still constrained by the fact that most of the data used for training and testing deep learning models are not available as FAIR and open data. As open science practices continue to grow in popularity, initiatives such as the FAIR data movement, open data, and open-source software have emerged. It is becoming increasingly important for researchers in the field of molecular informatics to embrace open science and to submit data and software that support their studies. With the advent of open-source deep learning frameworks and cloud computing platforms, academic researchers are now able to deploy and test their own deep learning algorithms with ease. With the development of new and faster hardware for deep learning and the increasing number of initiatives towards digital research data management infrastructures, as well as a culture promoting open data, open source, and open science, AI-driven molecular informatics will continue to grow. This review examines the current state of open data and open algorithms in molecular informatics, as well as ways in which they could be improved in future.

“…In general, rule-based tools work better with clean images, whereas slight distortions may hinder their performance 15 . In recent years, deep-learning-based OCSR tools have been developed 16,17,18 in conjunction with remarkable advancements in computer vision and natural language processing 19,20 . While several publications have claimed to have developed tools that are capable of recognizing chemical depictions with high accuracy, most of these tools are either proprietary or entirely unavailable 16,[21][22][23] .…”

Section: Introductionmentioning

confidence: 99%

DECIMER.ai - An open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications

Agea

et al. 2023

Preprint

The number of publications describing chemical structures has increased steadily over the last decades. However, the majority of published chemical information is currently not available in machine-readable form in public databases. It remains a challenge to automate the process of information extraction in a way that requires less manual intervention - especially the mining of chemical structure depictions. As an open-source platform that leverages recent advancements in deep learning, computer vision, and natural language processing, DECIMER.ai (Deep lEarning for Chemical ImagE Recognition) strives to automatically segment, classify, and translate chemical structure depictions from the printed literature. The segmentation and classification tools are the only openly available packages of their kind, and the optical chemical structure recognition (OCSR) core application yields outstanding performance on all benchmark datasets. The source code, the trained models and the datasets developed in this work have been published under permissive licences. An instance of the DECIMER web application is available at https://decimer.ai.