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Read-across is a popular data gap filling technique within category and analogue approaches for regulatory purposes. Acceptance of read-across remains an ongoing challenge with several efforts underway for identifying and addressing uncertainties. Here we demonstrate an algorithmic, automated approach to evaluate the utility of using in vitro bioactivity data (“bioactivity descriptors”, from EPA's ToxCast program) in conjunction with chemical descriptor information to derive local validity domains (specific sets of nearest neighbors) to facilitate read-across for up to ten in vivo repeated dose toxicity study types. Over 3239 different chemical structure descriptors were generated for a set of 1778 chemicals and supplemented with the outcomes from 821 in vitro assays. The read-across prediction of toxicity for 600 chemicals with in vivo data was based on the similarity weighted endpoint outcomes of its nearest neighbors. The approach enabled a performance baseline for read-across predictions of specific study outcomes to be established. Bioactivity descriptors were often found to be more predictive of in vivo toxicity outcomes than chemical descriptors or a combination of both. This generalized read-across (GenRA) forms a first step in systemizing read-across predictions and serves as a useful component of a screening level hazard assessment for new untested chemicals.Traditional read-across is expert driven and involves subjective selection of analogues and data interpretation.Propose generalized read-across, called GenRA, to predict similarity-weighted toxicity of chemicals in local domains.GenRA uses chemical structure and ToxCast bioactivity measure similarity.Evaluate GenRA toxicity predictions objectively for local domains using receiver operating characteristic (ROC) curve.Compare predictive performance of GenRA using chemical and bioactivity descriptors across local domains.