Teodora E. Harsa, Alexandra M. Harsa, Beata Szefler
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QSAR of caffeines by similarity cluster prediction
AbstractA novel QSAR approach based on correlation weighting and alignment over a hypermolecule that mimics the investigated correlational space was performed on a set of 40 caffeines downloaded from the PubChem database. The best models describing log P and LD50 values of this set of caffeine derivatives were validated against the external test set and in a new predictive model by using clusters of similarity.
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