Omkar2118/datasets huggingface dataset:
USPTO-30K

We introduce USPTO-30K, a large-scale benchmark dataset of annotated molecule images, which overcomes these limitations. It is created using the pairs of images and MolFiles by the United States Patent and Trademark Office. Each molecule was independently selected among all the available documents from 2001 to 2020. The set consists of three subsets to decouple the study of clean molecules, molecules with abbreviations and large molecules.

USPTO-10K contains 10,000 clean molecules, i.e. without any abbreviated groups.
USPTO-10K-abb contains 10,000 molecules with superatom groups.
USPTO-10K-L contains 10,000 clean molecules with more than 70 atoms.

Introducing paper: MolGrapher: Graph-based Visual Recognition of Chemical Structures
MolGrapher-Synthetic-300K

The set is created using molecule SMILES retrieved from the database PubChem. Images are then generated from SMILES using the molecule drawing library RDKit. The synthetic set is augmented at multiple levels:

Molecule level: Molecules are randomly transformed by: (1) displaying explicit hydrogens, (2) reducing of the size of bonds connected to explicit hydrogens, (3) displaying explicit methyls, (4) displaying explicit carbons, (5) selecting a molecular conformation, (6) removing implicit hydrogens of atom labels, (7) rotating triple bonds, (8) displaying explicit carbons connected to triple bonds, adding artificial superatom groups with (9) single or (10) multiple attachment points, (11) displaying wedge bonds using solid or dashed bonds, and (12) displaying single bonds as wavy bonds.
Rendering level: The rendering parameters used in RDKit are randomly set: (1) the bond width, (2) the font, (3) the font size, (4) the atom label padding, (5) the molecule rotation, which does not rotate atom labels, (6) the display of atom indices and (7) their font size, (8) the hand-drawing style, (9) the charges positions, (10) the display of encircled charges and (11) their size, and (12) the display of aromatic cycles using circles.

Introducing paper: MolGrapher: Graph-based Visual Recognition of Chemical Structures