Developmental toxicity is key human health endpoint, especially relevant for safeguarding maternal and child well-being. It is an object of increasing attention from international regulatory bodies such as the US EPA (US Environmental Protection Agency) and ECHA (European CHemicals Agency). In this challenging scenario, non-test methods employing explainable artificial intelligence based techniques can provide a significant help to derive transparent predictive models whose results can be easily interpreted to assess the developmental toxicity of new chemicals at very early stages. To accomplish this task, we have developed web platforms such as TIRESIA and TISBE. Based on a benchmark dataset, TIRESIA employs an explainable artificial intelligence approach combined with SHAP analysis to unveil the molecular features responsible for calculating the developmental toxicity. Descending from TIRESIA, TISBE employs a larger dataset, an explainable artificial intelligence framework based on a fragment-based fingerprint encoding, a consensus classifier, and a new double top-down applicability domain. We report here some practical examples for getting started with TIRESIA and TISBE.