This methodological paper presents a system for digitizing chemical images into SDF files. It utilizes a custom vectorization algorithm and chemical rule validation, achieving 94% accuracy on benchmark datasets compared to 50% for commercial tools.
This paper presents ChemReader, a fully automated optical structure recognition tool that converts raster images of chemical diagrams into machine-readable formats. It introduces a modified Hough transform for bond detection and a chemical spell checker that improves OCR accuracy from 66% to 87%.
An early method paper (AAAI ‘07) proposing a multi-stage sketch recognition pipeline. It introduces a domain verification step that uses chemical rules to refine ink parsing, achieving a 27% error reduction over geometric-only baselines.
A 2021 image-to-text approach treating OCSR as an image captioning task. It uses Transformers with SELFIES representation to convert molecular structure diagrams into SMILES strings, enabling extraction of visual chemical knowledge from scientific literature.
This 1992 paper introduces Kekulé, one of the first complete Optical Chemical Structure Recognition (OCSR) systems. It details a pipeline integrating raster-to-vector conversion, neural network-based OCR, and rule-based logic to convert printed chemical diagrams into connection tables.
A comprehensive categorization of OCSR methods, organizing techniques by their fundamental approach: deep learning, traditional ML, and rule-based systems.
This paper describes an early prototype system that digitizes chemical structure diagrams from scanned documents. It employs a multi-stage pipeline involving convex bounding polygon extraction, vectorization, and rule-based heuristics to generate MDL Molfiles.
A 2024 Nature Machine Intelligence paper providing causal evidence that invalid SMILES generation improves chemical language model performance by filtering low-likelihood samples, while validity constraints (as in SELFIES) introduce structural biases that impair distribution learning.
This 2022 perspective paper reviews 250 years of chemical notation evolution and proposes 16 concrete research projects to extend SELFIES beyond traditional organic chemistry into polymers, crystals, and reactions.
A fault-tolerant RDKit wrapper treating molecular visualization as a software engineering problem, implementing strategy pattern for SVG generation with automatic raster fallback, native SELFIES support for generative AI workflows, and strict type safety for reliable batch processing of millions of molecules in training pipelines.
A comprehensive 2020 analysis of the tautomerism problem in chemical databases, compiling 86 tautomeric transformation rules (20 existing, 66 new) and validating them across 400M+ structures to inform algorithmic improvements for InChI V2.
A comprehensive 2013 review explaining how InChI emerged as the global standard for chemical structure identifiers, covering its history as a response to the Internet’s need for non-proprietary molecular linking, its governance under IUPAC, and the technical layers that ensure uniqueness across diverse chemical databases.