An in-house library of 200 molecules from natural plant products was designed in order to
evaluate their binding to Plasmodium ACP enoyl reductase (ENR), a promising biological target for
antimalarial chemotherapeutics. The binding site of PfENR was explored computationally and the
molecules were docked using AutoDock. Furthermore, the top-ranked scaffolds from docking studies
were also compared with known PfENR inhibitors using 3D-QSAR. To this effect, a 3D-QSAR model
was derived from a set of experimentally established PfENR inhibitors, using Comparative Molecular
Force Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA).
The best optimum CoMFA model exhibited a leave-one-out correlation coefficient (q2) and a noncross-
validated correlation coefficient (r2) of 0.630 and 0.911, respectively. The result of this cumulative approach
proposed five structurally distinct natural products as potent PfENR inhibitors. This study may lay a stepping stone
towards Functional oriented synthesis (FOS) of novel PfENR inhibitors in future.
Keywords: Malaria, Plasmodium ACP enoylreductase (FabI), Molecular docking, CoMFA analysis, drug design.
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