Lennard-Jones’s 1932 theoretical paper applying quantum mechanical potential energy surfaces to gas-solid interactions, providing the first unified framework explaining both physisorption and chemisorption as different regions of the same energy landscape.
GDB-13 contains nearly 1 billion systematically generated small organic molecules with up to 13 atoms, achieving billion-scale chemical space exploration while maintaining drug-like properties.
GDB-17 contains 166.4 billion systematically generated small organic molecules with up to 17 atoms. It represents the most comprehensive exploration of drug-relevant chemical space achieved through computational enumeration.
Get a practical overview of the GEOM dataset and learn how it’s advancing 3D molecular machine learning by bridging static graphs and dynamic reality.
SubGrapher introduces a visual fingerprinting approach to Optical Chemical Structure Recognition that detects functional groups directly from images, enabling chemical database searches without full structure reconstruction and handling complex patent images including Markush structures.
Proposes Ring-Free Language (RFL) to hierarchically decouple molecular graphs into skeletons, rings, and branches, solving issues with 1D serialization of complex 2D structures. Introduces the Molecular Skeleton Decoder (MSD) to progressively predict these components, achieving strong results on handwritten and printed chemical structure recognition benchmarks.
An end-to-end data factory for molecular machine learning that transforms raw chemical formulas (e.g., C6H14) into labeled 3D conformer datasets, using MAYGEN for structural isomer enumeration, RDKit for 3D embedding, and physics-based featurization to address data scarcity in computational drug discovery.
Can mathematical signatures capture molecular shape? We test whether Coulomb matrix eigenvalues can distinguish alkane constitutional isomers, from unsupervised clustering failures to supervised learning successes.
A practical introduction to Coulomb matrices: how they transform molecular 3D structures into ML features, complete with Python examples and honest assessment of their limitations.