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Biodegradability prediction

Biodegradability prediction is biologically-inspired computing and attempts to predict biodegradability of anthropogenic materials in the environment. Demand for biodegradability prediction is expected to increase with governments stepping up environmental regulations (see, for instance, testing for bioaccumulation in the REACH proposal).


  • Development of quantitative structure-activity relationship (QSARs) for biodegradation, for instance, biochemical oxygen demand for chemicals released into the environment with the aid of machine learning and other artificial intelligence methods[1].
  • The University of Minnesota Biocatalysis and Biodegradation Database (UM-BBD), which contains information on microbial biocatalytic reactions and biodegradation pathways for primarily xenobiotic, chemical compounds. One of its many features allows the prediction of microbial catabolic reactions using substructure searching, a rule-base, and atom-to-atom mapping.

See also


  1. ^ Artificial Intelligence & biodegradability paper James R. Baker, Dragan Gamberger, James R. Mihelcic and Aleksandar Sabljić (2004) "Evaluation of Artificial Intelligence Based Models for Chemical Biodegradability Prediction", Molecules , 9, 989-1004
This article is licensed under the GNU Free Documentation License. It uses material from the Wikipedia article "Biodegradability_prediction". A list of authors is available in Wikipedia.
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