Generation of 2D-QSAR and pharmacophore models for fishing better anti-leishmanial therapeutics
by Clayton Fernando Rencilin; Joseph Christina Rosy; Krishnan Sundar
International Journal of Computational Biology and Drug Design (IJCBDD), Vol. 15, No. 4, 2023

Abstract: Leishmaniasis, a life-threatening tropical disease that is endemic in nearly 100 countries, contributes to millions of deaths each year. However, very few antileishmanial compounds are available in the market and that too possess many drawbacks. Hence, the therapeutic arsenal requires potential and novel anti-leishmanial compounds to treat Leishmaniasis. In the present study, quantitative structure activity relationship (QSAR) model and Pharmacophore model were developed with a set of antileishmanial compounds collected from literature and commercial antileishmanial drugs. A ligand-based pharmacophore model was developed using active compound as template and it was used for searching the purchasable compound dataset of ZINC database for matching compounds. Thirteen novel, readily purchasable compounds were obtained from this approach, which shows good predicted activity, ADME and druglikeness. These compounds can be regarded as potential candidates to be developed as novel antileishmanial drugs with improved activity and reduced side effects.

Online publication date: Mon, 17-Apr-2023

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