The min-max composition rule and its superiority over the usual max-min composition rule
Fuzzy Sets and Systems
Pattern Recognition in Chemistry
Pattern Recognition in Chemistry
Clustering Using a Similarity Measure Based on Shared Near Neighbors
IEEE Transactions on Computers
Biomedical Data and Applications
Biomedical Data and Applications
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Classification algorithms are proposed based on information entropy. The feasibility of mixing a given human immunodeficiency virus (HIV) inhibitor with dissimilar ones is studied. The 31 inhibitors are classified by their structural chemical properties. Many classification algorithms are based on information entropy. An excessive number of results appear compatible with the data and suffer combinatorial explosion. However, after the equipartition conjecture one has a selection criterion. According to this conjecture, the best configuration is that in which entropy production is most uniformly distributed. The structural elements of an inhibitor can be ranked according to their inhibitory activity. In didanosine (ddI) the base is a guanine derivative and the furan contains only one O heteroatom; ddI is selected as reference. In most inhibitors the furan contains one O heteroatom. The analysis is in agreement with principal component analysis and compares well with other classification taken as good based on docking, etc.