A machine learning approach to molecular structure recognition in chemical literature
Name
1128813865-MIT.pdf
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1.88 MB
Format
Adobe PDF
Checksum (MD5)
a0561ccb32ae574415325640e61efd01
Author(s)
Tabchouri, Sophia.
Advisor(s)
Regina Barzilay.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
Reaction diagrams in chemistry papers contain essential reaction information that is not available in the text. In order to extract comprehensive reaction information from chemistry literature, it is vital to convert these diagrams into a format compatible with searchable cheminformatic databases. Existing methods rely on rule-based procedures that have difficulty generalizing to noisy or different styled images. In this thesis, I implement a deep learning pipeline for identifying molecules in chemical diagrams and 'translating' the images into their corresponding SMILES strings. Diagram segmentation is performed using Mask R-CNN trained on an automatically generated set of diagrams. Translation to SMILES strings is performed using a neural machine translation model augmented with domain adaptation. Experimental results suggest that this model outperforms both rule-based and machine learning based models on diagrams extracted from real chemical literature.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 53-55).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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