Visceral leishmaniasis affects millions of people worldwide in areas where Leishmania donovani is endemic. The protozoan species serves a greater threat as it has gradually evolved drug resistance whereby requiring newer approaches to treat the infection. State-of-art techniques are mostly directed toward finding better targets extracted from the available proteome data. In light of recent computational advancements, we ascertain and validate one such target, adenylosuccinate lyase (ADSL) by implementation of in-silico methods which led to the identification of critical amino acid residues that affects its functional attributes. Our target selection was based on comprehensive topological analysis of a knowledge-based protein-protein interaction network. Subsequently, mutations were incorporated and the dynamic behavior of mutated and native proteins was traced using MD simulations for a total time span of 600 ns. Comparative analysis of the native and mutated structures exhibited perceptible changes in the ligand-bound catalytic region with respect to time. The unfavorable changes in the orientations of specific catalytic residues, His118 and His196, induced by generated mutations reduce the enzyme specificity. In summary, this integrative approach is able to select a target against pathogen, identify crucial residues, and challenge its functionality through the selected mutations.