Markov Logic Networks for Optical Chemical Structure Recognition

Gabbrielli, Francesco; Lippi, Marco; Marinai, Simone

doi:10.1021/ci5002197

Cited by 27 publications

(19 citation statements)

References 31 publications

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“…Moreover, as the need for harvesting the large amounts of published data grows, the demand for methods for easily mining structures from papers and patent data is also growing. Optical Character Recognition (OCR) systems, relying on a variety of ML and probabilistic pattern recognition techniques, were created to translate 2D depictions of chemical structures to standard chemical representations [146][147][148]. Nonetheless, the development of OCR systems can be hindered by the images' resolutions, the computational interpretations of chemical abbreviations, and the nature of the image representation, which can be embedded in text, in figures containing multiple structures, or in reaction pathways, and can be represented as either a skeletal formula or a Markush structure.…”

Section: Graphical Representations For Molecules and Macromoleculesmentioning

confidence: 99%

Molecular representations in AI-driven drug discovery: a review and practical guide

et al. 2020

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The technological advances of the past century, marked by the computer revolution and the advent of high-throughput screening technologies in drug discovery, opened the path to the computational analysis and visualization of bioactive molecules. For this purpose, it became necessary to represent molecules in a syntax that would be readable by computers and understandable by scientists of various fields. A large number of chemical representations have been developed over the years, their numerosity being due to the fast development of computers and the complexity of producing a representation that encompasses all structural and chemical characteristics. We present here some of the most popular electronic molecular and macromolecular representations used in drug discovery, many of which are based on graph representations. Furthermore, we describe applications of these representations in AI-driven drug discovery. Our aim is to provide a brief guide on structural representations that are essential to the practice of AI in drug discovery. This review serves as a guide for researchers who have little experience with the handling of chemical representations and plan to work on applications at the interface of these fields.

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Section: Graphical Representations For Molecules and Macromoleculesmentioning

confidence: 99%

Molecular representations in AI-driven drug discovery: a review and practical guide

et al. 2020

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“…MLOCSR [33] is an OCSR method that follows a pipelined design strategy which is a combination of low level and high-level processing. The workflow is divided into three modules.…”

Section: Markov Logic Network For Ocsrmentioning

confidence: 99%

A review of optical chemical structure recognition tools

Rajan

Brinkhaus

Zielesny³

et al. 2020

J Cheminform

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Structural information about chemical compounds is typically conveyed as 2D images of molecular structures in scientific documents. Unfortunately, these depictions are not a machine-readable representation of the molecules. With a backlog of decades of chemical literature in printed form not properly represented in open-access databases, there is a high demand for the translation of graphical molecular depictions into machine-readable formats. This translation process is known as Optical Chemical Structure Recognition (OCSR). Today, we are looking back on nearly three decades of development in this demanding research field. Most OCSR methods follow a rule-based approach where the key step of vectorization of the depiction is followed by the interpretation of vectors and nodes as bonds and atoms. Opposed to that, some of the latest approaches are based on deep neural networks (DNN). This review provides an overview of all methods and tools that have been published in the field of OCSR. Additionally, a small benchmark study was performed with the available open-source OCSR tools in order to examine their performance.

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“…Over the following decades, more complex systems were developed, often based on the principles of their predecessors. [18][19][20][21][22][23][24][25][26][27][28][29] OSRA was the first chemical structure recognition open-source software, allowing new programs to be developed by direct extension.…”

Section: Introductionmentioning

confidence: 99%

“…The majority of optical chemical structure recognition packages, including Kekulé, 15 IBM's OROCS, 16 CLiDE 17 and CLiDEPro, 22 ChemOCR, 21 OSRA, 23 ChemReader, 24 MolRec, 26 ChemEx, 27 MLOCSR, 28 and ChemSchematicResolver 29 rely on a rule-based workflow rather than a data-driven approach. These systems achieve various degrees of accuracy, with the recently developed ChemSchematicResolver reaching 83-100% precision on a range of datasets.…”

Section: Introductionmentioning

confidence: 99%

ChemPix: Automated Recognition of Hand-drawn Hydrocarbon Structures Using Deep Learning

Weir¹,

Thompson²,

Choi³

et al. 2021

Preprint

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<p>Inputting molecules into chemistry software, such as quantum chemistry packages, currently requires domain expertise, expensive software and/or cumbersome procedures. Leveraging recent breakthroughs in machine learning, we develop ChemPix: an offline, hand-drawn hydrocarbon structure recognition tool designed to remove these barriers. A neural image captioning approach consisting of a convolutional neural network (CNN) encoder and a long short-term memory (LSTM) decoder learned a mapping from photographs of hand-drawn hydrocarbon structures to machine-readable SMILES representations. We generated a large auxiliary training dataset, based on RDKit molecular images, by combining image augmentation, image degradation and background addition. Additionally, a small dataset of ~600 hand-drawn hydrocarbon chemical structures was crowd-sourced using a phone web application. These datasets were used to train the image-to-SMILES neural network with the goal of maximizing the hand-drawn hydrocarbon recognition accuracy. By forming a committee of the trained neural networks, we achieved a nearly 10 percentage point improvement of the molecule recognition accuracy and were able to assign a confidence value for the prediction based on the number of agreeing votes. The top ensemble model achieved a hand-drawn hydrocarbon recognition accuracy of 77% for the first prediction and 86% if the top 3 predictions were considered; in over 50% of cases, the model was at least 97% confident in the prediction, making it a promising tool for real-world use cases.</p>

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Markov Logic Networks for Optical Chemical Structure Recognition

Cited by 27 publications

References 31 publications

Molecular representations in AI-driven drug discovery: a review and practical guide

Molecular representations in AI-driven drug discovery: a review and practical guide

A review of optical chemical structure recognition tools

ChemPix: Automated Recognition of Hand-drawn Hydrocarbon Structures Using Deep Learning

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