In order to systematically approach the design of chemicals with minimal toxicity we are in need of predictive tools that can be applied seamlessly during chemical synthesis and characterization. Our approach is to develop models that utilize spectroscopic data, called Quantitative Spectrometric Data-Activity Relationships, to predict bioavailability and toxicity. Such models do not require knowledge of chemical structure and can be applied to chemical mixtures. Here we report a predictive QSDAR for skin permeation rate (log K_p) of organic chemicals from NMR spectroscopic data and molecular weight. The model is trained on a large data set consisting of structurally diverse chemicals and has been thoroughly externally validated – once with a withheld subset of the original data set, and once with a distinct set of complex biologically active compounds curated by Klimisch scoring (r² = 0.838, q_ext1² = 0.837, q_ext2² = 0.419). The model performs equally or better than prevailing structure-based methods, and offers a number of advantages for facilitating rational design of safer chemicals.