In studies of vision and audition, stimuli can be systematically varied by wavelength and frequency, respectively, but there is no equivalent metric for olfaction. Restricted odorant-feature metrics such as number of carbons and functional group do not account for response patterns to odorants varying along other structural dimensions. We generated a multidimensional odor metric, in which each odorant molecule was represented as a vector of 1,664 molecular descriptor values. Revisiting many studies, we found that this metric and a second optimized metric were always better at accounting for neural responses than the specific metric used in each study. These metrics were applicable across studies that differed in the animals studied, the type of olfactory neurons tested, the odorants applied and the recording methods used. We use this new metric to recommend sets of odorants that span the physicochemical space for use in olfaction experiments.