We have used a differential evolution-binary particle swarm  optimization (DE-BPSO) feature selection method to develop a QSAR model  for 91 structurally diverse �-diketo acids which are potent HIV-1  integrase inhibitors. DE-BPSO is a novel feature selection method which  requires fewer generations than BPSO to select a good small subset of  descriptors. These descriptors are then used for developing QSAR models.  387 constitutional, geometrical, topological, electrostatic, and  quantum-chemical descriptors were computed for each of the 91 structures  and QSAR models were developed. The top ranked model satisfying  predictive statistical constraints (r2 > 0.6, r2validation > 0.5, r2test > 0.5) was considered for analysis of the physiochemical features of  �-diketo acids conducive for inhibition of HIV-1 integrase. The model  suggests that molecular volume of the chemical compounds plays a  dominant role in the inhibition of HIV-1 integrase. We also used this  model successfully as a virtual screening tool to predict the biological  activities closer to their experimental values of 18 2-pyrrolinone  derivatives and 32 rhodanine containing compounds. This model can be  used to identify novel compounds which may have similar structural  properties as �-diketo acids with inhibitory effects towards HIV-1  integrase. We believe that DE-BPSO is a novel feature selection method  for QSAR model development of other chemical compounds.

Evolutionary computational modeling of β-diketo acids for virtual screening of HIV-1 integrase inhibitors

243rd National Meeting (2012)

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