Background:　HIV-1　protease　inhibitor　(PIs)　is　a　good　choice　for　AIDS　patients.　Nevertheless,　for　PIs,　there　are　several　bugs　in　clinical　application,　like　drug　resistance,　the　large　dose,　the　high　costs　and　so　on,　among　which,　the　poor　pharmacokinetics　property　is　one　of　the　important　reasons　that　leads　to　the　failure　of　its　clinical　application.　Objective:　We　aimed　to　build　computational　models　for　studying　the　relationship　between　PIs　structure　and　its　pharmacological　activities.　Method:　We　collected　experimental　values　of　koff/Ki　and　structures　of　50　PIs　through　a　careful　literature　and　database　search.　Quantitative　structure　activity/pharmacokinetics　relationship　(QSAR/QSPR)　models　were　constructed　by　support　vector　machine　(SVM),　partial-least　squares　regression　(PLSR)　and　back-propagation　neural　network　(BPNN).　Results:　For　QSAR　models,　SVM,　PLSR　and　BPNN　all　generated　reliable　prediction　models　with　the　r(2)　of　0.688,　0.768　and　0.787,　respectively,　and　r(pred)(2)　of　0.748,　0.696　and　0.640,　respectively.　For　QSPR　models,　the　optimum　models　of　SVM,　PLSR　and　BPNN　obtained　the　r(2)　of　0.952,　0.869　and　0.960,　respectively,　and　the　r(pred)(2)　of　0.852,　0.628　and　0.814,　respectively.　Conclusion:　Among　these　three　modelling　methods,　SVM　showed　superior　ability　than　PLSR　and　BPNN　both　in　QSAR/QSPR　modelling　of　PIs,　thus,　we　suspected　that　SVM　was　more　suitable　for　predicting　activities　of　PIs.　In　addition,　3D-MoRSE　descriptors　may　have　a　tight　relationship　with　the　K-i　values　of　PIs,　and　the　GETAWAY　descriptors　have　significant　influence　on　both　k(off)　and　K-i　in　PLSR　equations.Y