LAUSR

In this study, we make QSAR models from 29 of BA derivatives’ HIV maturation inhibition activities against their 3D descriptors. The best model involve 5 descriptors as follows: 1/log EC50 = -462.275 + (69.213 × TDB6u) + (723.745 × TDB6e) + (-0.576 × FPSA-3) + (0.849 × RDF140u) + (0.302 × RDF80e) r2 training = 0.7918; Q2 test = 0.9644; r2test = 0.9798; and r2m-test = 0.9445 TDB6u and TDB6e are the 3d topological distance-based autocorrelation-lag 6 /unweighted and weighted by Sanderson electronegati-vities, respectively. FPSA-3 is the value of charge weighted partial positive surface area / total molecular surface area. RDF140u is radial distribution function-140 / unweighted. RDF80e is radial distribution function-080/weighted by relative Sanderson electronegativities. The QSAR model was then used to design and predict some of the new BA derivatives’ HIV maturation activities. The best predicted compound had pEC50 value of -0.838 and EC50 value of 0.064 nM with the chemical IUPAC name of 4‐[(1R, 3aR, 5aR, 5bR, 7aS, 11aR, 11bS, 13aS,13bS)‐5a, 5b, 8, 8, 11b pentamethyl‐1‐(prop‐1‐en‐2‐yl)‐3a[({2‐[4‐(pyrimidin2yl)piperazin-1-yl]ethyl}amino) methyl]‐icosahydro‐1H-cyclopenta[a]chrysen‐9‐yl]benzoic acid. We also suggest the synthetic route to the proposed compound.