Read-across is a popular data gap filling technique used within analogue and category approaches for regulatory purposes. In recent years there have been many efforts focused on the challenges involved in read-across development, its scientific justification and documentation. Tools have also been developed to facilitate read-across development and application. Here, we describe a number of publicly available read-across tools in the context of the category/analogue workflow and review their respective capabilities, strengths and weaknesses. No single tool addresses all aspects of the workflow. We highlight how the different tools complement each other and some of the opportunities for their further development to address the continued evolution of read-across.

ducible read-across predictions of toxicity outcomes from in vivo studies (Shah et al., 2016). The read-across prediction is a similarity weighted activity of source analogues (nearest neighbors) based on chemistry and/or bioactivity descriptors. The approach is a generalization of the Chemical Biological Read-Across (CBRA) approach published by Low et al. (2013). GenRA has been described in more detail in the literature (Shah et al., 2016;. Here we outline the principles of the approach and its workflow implementation in the EPA CompTox Chemicals Dashboard (Williams et al., 2017; 1 ). Materials and methodsThe GenRA framework has been implemented using a classical three tier architecture, which is seamlessly embedded in the EPA CompTox Chemicals Dashboard, and includes: 1) a web-based presentation tier; 2) an application tier based on representational

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