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Compound prioritization from inverse docking experiment using receptor‐centric and ligand‐centric methods: a case study on Plasmodium falciparum Fab enzymes
Author(s) -
Kumar Sivakumar Prasanth,
Pandya Himanshu A.,
Desai Vishal H.,
Jasrai Yogesh T.
Publication year - 2014
Publication title -
journal of molecular recognition
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 79
eISSN - 1099-1352
pISSN - 0952-3499
DOI - 10.1002/jmr.2353
Subject(s) - docking (animal) , computational biology , cluster analysis , computer science , prioritization , protein–ligand docking , artificial intelligence , data mining , machine learning , chemistry , drug discovery , biology , virtual screening , biochemistry , engineering , medicine , nursing , management science
Prioritization of compounds using inverse docking approach is limited owing to potential drawbacks in its scoring functions. Classically, molecules ranked by best or lowest binding energies and clustering methods have been considered as probable hits. Mining probable hits from an inverse docking approach is very complicated given the closely related protein targets and the chemically similar ligand data set. To overcome this problem, we present here a computational approach using receptor‐centric and ligand‐centric methods to infer the reliability of the inverse docking approach and to recognize probable hits. This knowledge‐driven approach takes advantage of experimentally identified inhibitors against a particular protein target of interest to delineate shape and molecular field properties and use a multilayer perceptron model to predict the biological activity of the test molecules. The approach was validated using flavone derivatives possessing inhibitory activities against principal antimalarial molecular targets of fatty acid biosynthetic pathway, FabG, FabI and FabZ, respectively. We propose that probable hits can be retrieved by comparing the rank list of docking, quantitative‐structure activity relationship and multilayer perceptron models. Copyright © 2014 John Wiley & Sons, Ltd.