In the field of chemical structure recognition, the task of converting molecular images into machine-readable data formats such as SMILES string stands as a significant challenge, primarily due to the varied drawing styles and conventions prevalent in chemical literature. To bridge this gap, we proposed MolNexTR, a novel image-to-graph deep learning model that collaborates to fuse the strengths of ConvNext, a powerful Convolutional Neural Network variant, and Vision-TRansformer. This integration facilitates a more detailed extraction of both local and global features from molecular images. MolNexTR can predict atoms and bonds simultaneously and understand their layout rules. It also excels at flexibly integrating symbolic chemistry principles to discern chirality and decipher abbreviated structures. We further incorporate a series of advanced algorithms, including an improved data augmentation module, an image contamination module, and a post-processing module for getting the final SMILES output. These modules cooperate to enhance the model's robustness to diverse styles of molecular images found in real literature. In our test sets, MolNexTR has demonstrated superior performance, achieving an accuracy rate of 81–97%, marking a significant advancement in the domain of molecular structure recognition. Scientific contribution MolNexTR is a novel image-to-graph model that incorporates a unique dual-stream encoder to extract complex molecular image features, and combines chemical rules to predict atoms and bonds while understanding atom and bond layout rules. In addition, it employs a series of novel augmentation algorithms to significantly enhance the robustness and performance of the model.

Yufan Chen

Ching Ting Leung

Yong Huang

Jianwei Sun

Hao Chen

Hanyu Gao

Journal of Cheminformatics

JOURNAL OF CHEMINFORMATICS 是一本开放获取期刊，发表化学信息学和分子建模各个方面的原始同行评议研究。覆盖范围包括但不限于：化学信息系统、软件和数据库，以及分子建模化学结构表示及其在结构中的应用、化学物质和化学反应数据库的子结构和相似性搜索计算机和分子图形、计算机辅助分子设计、专家系统、QSAR 和数据挖掘技术。

The Journal of Cheminformatics is an open-access journal dedicated to original peer-reviewed research in all aspects of cheminformatics and molecular modeling. Its scope includes, but is not limited to: chemical information systems, software and databases, molecular modeling of chemical structure representations and their applications, substructure and similarity searching in chemical substance and reaction databases, computer and molecular graphics, computer-aided molecular design, expert systems, QSAR (Quantitative Structure-Activity Relationship), and data mining techniques.

《Journal of Cheminformatics》是一本开放获取的期刊，专注于化学信息学和分子建模领域的原创性同行评审研究。期刊的覆盖范围包括但不限于：化学信息系统、软件和数据库，以及分子建模中的化学结构表示及其应用、化学物质和化学反应数据库的子结构和相似性搜索、计算机和分子图形、计算机辅助分子设计、专家系统、QSAR（定量构效关系）和数据挖掘技术。

Category	Quartile
化学	2
化学, 化学综合	2
化学, 计算机信息系统	2
化学, 计算机跨学科应用	3

MolNexTR: a generalized deep learning model for molecular image recognition

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Journal Info

Category	Quartile
COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS	1