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Advancement in the field of computational prediction of numerous chemical toxicities has grown over the past two decades significantly. These in silico methods offer significant benefits to both regulatory needs and requirements for risk assessments, and the pharmaceutical industry to assess the safety profile of a chemical. Here, we present ProTox II and StopTox that incorporates molecular similarity, pharmacophores, fragment propensities, and machine-learning models for the prediction of various toxicity; such as acute toxicity, hepatotoxicity, cytotoxicity, carcinogenicity, mutagenicity, immunotoxicity, and toxicity targets. STopTox and ProTox-II provide a freely obtainable webserver for in silico toxicity prediction for toxicologists, regulatory agencies, computational and medicinal chemists, and all users without login at https://stoptox.mml.unc.edu/ and http://tox.charite.de/protox_II. The webserver takes a two-dimensional chemical structure as input and reports the possible toxicity profile of the chemical for 33 models with confidence scores, and an overall toxicity radar chart along with the three most similar compounds with known acute toxicity.