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WIREs Comput Mol Sci
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In silico toxicology: computational methods for the prediction of chemical toxicity

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Determining the toxicity of chemicals is necessary to identify their harmful effects on humans, animals, plants, or the environment. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. However, in vivo animal tests are constrained by time, ethical considerations, and financial burden. Therefore, computational methods for estimating the toxicity of chemicals are considered useful. In silico toxicology is one type of toxicity assessment that uses computational methods to analyze, simulate, visualize, or predict the toxicity of chemicals. In silico toxicology aims to complement existing toxicity tests to predict toxicity, prioritize chemicals, guide toxicity tests, and minimize late‐stage failures in drugs design. There are various methods for generating models to predict toxicity endpoints. We provide a comprehensive overview, explain, and compare the strengths and weaknesses of the existing modeling methods and algorithms for toxicity prediction with a particular (but not exclusive) emphasis on computational tools that can implement these methods and refer to expert systems that deploy the prediction models. Finally, we briefly review a number of new research directions in in silico toxicology and provide recommendations for designing in silico models. WIREs Comput Mol Sci 2016, 6:147–172. doi: 10.1002/wcms.1240

In silico toxicology tools, steps to generate prediction models, and categories of prediction models.
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Summary of methods to predict toxicity of chemicals.
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Flowchart of in silico prediction models.
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Four regions in SAR maps are scaffold hops, smooth region, nondescript, and activity cliffs.
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SAR landscapes.
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2D scatter plots of molecular descriptors and toxicity levels. (a) no correlation between molecular descriptor 1 and the toxicity endpoint. (b) and (c) linear and nonlinear relationships between the molecular descriptors 2 and 3, respectively, with the toxicity endpoint. (b) and (c) can be modeled with linear and nonlinear algorithms, respectively.
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Bliss method. (a) Plot mortality frequency (the number of dead subjects) versus dose or time. (b) Convert frequency to percentages (percentage of deceased subjects). (c) Transform percentages to probits and transform dose or time to logarithms.
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Different types of relationships for dose–response models. Similar relationships can be generated for time–response models.
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Different properties of read‐across models.
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Different approaches of read‐across: analog versus category approaches, interpolation versus extrapolation, category boundary and outliers.
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