A 2023 software update paper documenting improvements to the SELFIES Python library (v2.1.1), including a streamlined context-free grammar, expanded support for aromatic systems and stereochemistry, customizable semantic constraints, ML utility functions, and performance benchmarks on 300K+ molecules.
A 2018 infrastructure paper introducing RInChI (Reaction InChI), the first standardized format for uniquely identifying chemical reactions through algorithmic hashing and layering, enabling reaction database searching and duplicate detection analogous to how InChI works for individual molecules.
The 2020 paper that introduced SELFIES: Mario Krenn and colleagues created a molecular representation that solves SMILES validity problems. It guarantees every generated string corresponds to a valid chemical structure.
David Weininger introduced SMILES notation in 1988, establishing encoding rules for representing chemical structures as compact, human-readable strings.
Performance evaluation of MolRec at the CLEF 2012 competition reveals a large performance gap between the automatic evaluation set (94-96% accuracy) and the manual evaluation set of complex patent structures (46-59% accuracy), with systematic analysis of failure modes including character grouping bugs, touching characters, and four-way junction vectorization.
Details the MolRec system for converting chemical diagram images into MOL files using vectorization, geometric rules, and graph construction. Achieved 95% accuracy on 1000 TREC 2011 benchmark images with comprehensive failure analysis of limitations.
Discover how OCSR technology bridges the gap between molecular images and machine-readable data, evolving from rule-based systems to modern deep learning models for chemical knowledge extraction.
A 2024 deep learning system for optical chemical structure recognition designed specifically for biomedical literature mining, using ResNet-Transformer architecture to handle challenging conditions including low-resolution images, noise, distortions, and even hand-drawn molecular diagrams from scientific documents.
A 2011 rule-based OCSR system designed specifically for the challenging low-quality images in Japanese patent applications, using segment-based methods to handle pervasive problems like touching characters, merged atom labels with bonds, and broken lines.
The MolParser project introduces two key datasets: MolParser-7M, the largest training dataset for Optical Chemical Structure Recognition (OCSR) with 7.7M pairs of images and E-SMILES strings, and WildMol, a new 20k-sample benchmark for evaluating models on challenging real-world data. The training data uniquely combines millions of diverse synthetic molecules with 400,000 manually annotated in-the-wild samples.
A 2025 end-to-end OCSR system addressing both technical and data challenges, introducing MolParser-7M (7M+ image-text pairs) and MolDet (YOLO-based detector) for extracting and recognizing molecular structures from real-world documents with diverse quality and styles.
Build a robust Python CLI tool that converts both SMILES and SELFIES notation into publication-quality 2D molecular images, complete with formulas and legends.