Most compounds in patents live only as images. This guide covers the end-to-end workflow for extracting them into SMILES, InChIKey and SDF, checking quality against the source page, and where Mollens fits.
Browsing: Mollens
OCSR converts chemical structure drawings in images and PDFs into SMILES, MOL/SDF, and InChIKeys. Learn how the pipeline works, how the field evolved, how accuracy is measured, and where Mollens fits.
A hands-on guide to turning chemical structures in PDFs into SDF, SMILES, and CSV files with Mollens, plus tips for clean inputs, output checks, and downstream use in RDKit, ELNs, and registration.
Patent examples are a rich public SAR source. Learn how to extract exemplified compounds as source-linked SMILES and SDF, join activity data from patent tables, and analyze scaffolds and substituents.
Many patent compounds exist only as drawings. See how extracted SMILES and InChIKeys support exact and similarity prior art searches, with each structure linked back to its source page.
A balanced comparison of OCSR tools: manual redrawing, open-source models (OSRA, DECIMER, MolScribe) and Mollens, with a criteria table and a checklist for choosing the right approach.
Published patent applications are the earliest view of a competitor’s chemical matter. Learn a CI and BD workflow to extract, cluster and compare competitor structures with Mollens.
Mollens is Patsnap’s AI chemical structure extraction tool. It turns patents and papers into source-linked SMILES, InChIKey and SDF packages. This overview covers how it works, accuracy, pricing and limits.