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Among the various known targets for the treatment of Leishmaniasis, dihydrofolate reductase (DHFR) is an essential target which plays an important role in the folate metabolic pathway. In the current study, pharmacoinformatics approaches including quantum chemistry methods, molecular docking and molecular dynamics simulations have been utilized to identify selective  Leishmania donovani  DHFR ( Ld DHFR) inhibitors. Initially, for the design of new  Ld DHFR inhibitors, a virtual combinatorial library was created by considering various head groups (scaffolds), linkers and tail groups. The scaffolds utilized in the library design were selected on the basis of their proton affinity (PA) estimated using quantum chemical methods, required to make a strong H-bond interaction with negatively charged  Ld DHFR active site. Later on, molecular docking-based virtual screening was performed to screen the designed library. Selectivity of the chosen hits toward the  Ld DHFR was established through re-docking in the human DHFR enzyme ( Hs DHFR). Best five hits were subjected to molecular dynamics (MD) simulations to validate their selectivity as well as stability in  Ld DHFR. Out of the five hits, four were found to be energetically more favorable and promising for selective binding toward  Ld DHFR in comparison to  Hs DHFR. Communicated by Ramaswamy H. Sarma

Identification of selective Ld DHFR inhibitors using quantum chemical and molecular modeling approach