Software like ChemDraw lets chemists draw chemical structures on computers, but drawing them by hand on a piece of paper is easier much of the time. While those drawings are convenient, they can’t be easily transferred to a computer to use computational tools. So Todd J. Martínez of SLAC National Accelerator Laboratory and Stanford University and colleagues developed a machine-learning technique that can recognize hand-drawn hydrocarbon structures from pictures and convert them to the computer-readable SMILES (simplified molecular-input line-entry system) format (ChemRxiv 2021, DOI: 10.26434/chemrxiv.14156957.v1). Called ChemPix, the tool uses neural network algorithms adapted from image captioning software. In the group’s tests, ChemPix could correctly identify hand-drawn molecules from photographs about 85% of the time. Hayley Weir, a PhD student at Stanford who led the work, says the team deliberately trained and tested the software on blurry, low-quality images to mimic real-world use. ChemPix can recognize aromatic rings, double bonds, and assumed hydrogens, but the researchers foresee differences in handwriting complicating efforts to extend its abilities to heteroatoms.