Abstract: Classification algorithms are proposed based on information entropy. It is studied the molecular classification of anti-human immunodeficiency virus thiocarbamates. The 62 thiocarbamates (TCs) 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 of a flowsheet 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 the order: B1/2 > R > R1 > R2 substitution. In TC 17, B1/2 = B1, R = 4-CH3 and R1 = R2 = H; its associated vector is unary. The TC 17 is selected as a reference. In some TCs B1/2 = B1, in some others B1/2 = B2. The analysis is in qualitative agreement with other classification taken as good based on k-means clustering. Program MolClas is a simple, reliable, efficient and fast procedure for molecular classification, based on the equipartition conjecture of entropy production. The structural elements allow the periodic classification of the TCs. A validation is performed with an external property, cytoprotection activity, not used in the development of the table.