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.