GDB-17 contains 166 billion systematically generated small organic molecules with up to 17 atoms, representing 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.
A provocative 2024 Nature Machine Intelligence paper challenging the assumption that invalid SMILES are failures, showing empirically that the ability to generate invalid outputs actually improves chemical language model performance by enabling quality filtering and providing richer training signals.
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.
A 2022 perspective paper by Krenn, Aspuru-Guzik, and colleagues reviewing 250 years of chemical notation evolution and proposing 16 concrete research projects to extend SELFIES beyond traditional organic chemistry into polymers, crystals, reactions, and other complex chemical systems where traditional representations break down.