Abstract
Secondary structures of amino acid sequences can be predicted with over 70% accuracy in Heidelberg with the aid of artificial neural networks. This improvement over the accuracy of statistical methods is extremely important in view of the rational design of peptides and proteins and the processing of data in sequence data banks. The potential of neural networks is thus demonstrated once again (see also the review “Neural Networks in Chemistry” by J. Gasteiger and J. Zupan in the April issue of Angewandte Chemie).
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