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FAst MEtabolizer (FAME) is a fast and accurate predictor of sites of metabolism (SoMs). It is based on a collection of random forest models trained on diverse chemical data sets of more than 20???000 molecules annotated with their experimentally determined SoMs. Using a comprehensive set of available data, FAME aims to assess metabolic processes from a holistic point of view. It is not limited to a specific enzyme family or species. Besides a global model, dedicated models are available for human, rat, and dog metabolism; specific prediction of phase I and II metabolism is also supported. FAME is able to identify at least one known SoM among the top-1, top-2, and top-3 highest ranked atom positions in up to 71%, 81%, and 87% of all cases tested, respectively. These prediction rates are comparable to or better than SoM predictors focused on specific enzyme families (such as cytochrome P450s), despite the fact that FAME uses only seven chemical descriptors. FAME covers a very broad chemical space, which together with its inter- and extrapolation power makes it applicable to a wide range of chemicals. Predictions take less than 2.5 s per molecule in batch mode on an Ultrabook. Results are visualized using Jmol, with the most likely SoMs highlighted.

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  FAME DOI is a collection of random forest models trained on a comprehensive and highly diverse data set of 20,000 small molecules annotated with their experimentally determined sites of metabolism taken from multiple species (rat, dog and human) designed...
  FAME DOI is a collection of random forest models trained on a comprehensive and highly diverse data set of 20,000 small molecules annotated with their experimentally determined sites of metabolism taken from multiple species (rat, dog and human) designed...
Whilst much computational work is undertaken to support, library design, virtual screening, hit selection and affinity optimisation the reality is that the most challenging issues to resolve in drug discovery often revolve around absorption, distribution, metabolism...
Whilst much computational work is undertaken to support, library design, virtual screening, hit selection and affinity optimisation the reality is that the most challenging issues to resolve in drug discovery often revolve around absorption, distribution, metabolism...
 Whilst much computational work is undertaken to support, library design, virtual screening, hit selection and affinity optimisation the reality is that the most challenging issues to resolve in drug discovery often revolve around absorption, distribution,...