  3\b{eta}-O-phthalic ester of betulinic acid is of great importance in
anticancer studies. However, the optimization of its reaction conditions
requires a large number of experimental works. To simplify the number of times
of optimization in experimental works, here, we use artificial neural network
(ANN) and support vector machine (SVM) models for the prediction of yields of
3\b{eta}-O-phthalic ester of betulinic acid synthesized by betulinic acid and
phthalic anhydride using lipase as biocatalyst. General regression neural
network (GRNN), multilayer feed-forward neural network (MLFN) and the SVM
models were trained based on experimental data. Four indicators were set as
independent variables, including time (h), temperature (C), amount of enzyme
(mg) and molar ratio, while the yield of the 3\b{eta}-O-phthalic ester of
betulinic acid was set as the dependent variable. Results show that the GRNN
and SVM models have the best prediction results during the testing process,
with comparatively low RMS errors (4.01 and 4.23respectively) and short
training times (both 1s). The prediction accuracy of the GRNN and SVM are both
100% in testing process, under the tolerance of 30%.
