Similarity searching in ligand-based virtual screening using different fingerprints and different similarity coefficients

Similarity searching plays an increasingly important role in virtual screening. It is a screening technique that works by comparing the features of the target compound with the features of each compound in the database of compounds. This comparison can be described in three steps. The first step involves the representation of the target compound and the database compounds with an equivalent representation, which is set of binary elements describing the presence or the absence of attributes of compounds (fingerprint). The second step uses similarity coefficient to calculate the score of similarity between two compounds representation. The third step is to rank the database compounds in appropriate order of the similarity score, in order to determine the actives compounds. Many approaches and techniques have been introduced in literature to enhancing and improving similarity-based virtual screening. In this work, our primary interests are to investigate the effect of using different combinations of fingerprint and similarity coefficient in ligand-based virtual screening (LBVS). We use in this investigation the MDDR (drug data report database) to evaluate the different combinations descriptor-coefficient. Some obtained results of combinations with some coefficients demonstrate superiority in performances to these obtained in combination with Tanimoto coefficient.