A combined strategy based on the development of pharmacophore hypotheses and NMR approaches is reported for the identification of novel inhibitors of heparanase,a key enzyme involved in tumor metastasis through the remodeling of the subepithelial and subendothelial basement membranes,resulting in the dissemination of metastatic cancer cells.Several pharmacophore hypotheses were initially developed from the most active heparanase inhibitors known to date and,after their application to a pool of 27 known heparanase inhibitors and a database of 1,120 compounds approved by the FDA,a four-point pharmacophore model was selected as the most predictive.This model was subsequently applied to a database of 686 chemical fragments,and a subset of 100 fragments accomplishing completely or partially the four-point model was selected to perform nuclear magnetic resonance experiments to validate the hypothesis.The experimental studies confirmed the reliability of our pharmacophore model,its applicability to in silico databases in order to reduce the number of compounds to be experimentally screened,and the possibility of generating fragment libraries enriched in heparanase inhibitors.