: In the last years, similarity searching has gained wide popularity as a method for performing ligand-based virtual screening (LBVS). This screening technique functions by making a comparison of the target compound’s features with that of each compound’s features in the database of compounds. It is well known that none of the individual similarity measure could provide the best performances each time pertaining to active compound structure representing all types of activity classes. In the literature, we find several techniques and strategies that have been proposed to improve the overall effectiveness of ligand-based virtual screening approaches. In this paper, a genetic algorithm-based feature selection approach is put forward to improve similarity searching pertaining to ligand-based virtual screening. In this study, we demonstrated how genetic algorithms can be applied to enable optimisation of screening process’s performance by choosing the most relevant features. Three different benchmark datasets taken from the MDDR (drug data report database) are employed to examine and assess the performance of our proposed approach. The obtained results demonstrate superiority in performances compared with these obtained with Tanimoto coefficient, which is considered as the most performing coefficient of the domain of LBVS.

No abstract

No abstract

No abstract