Prediction of the Yield of Enzymatic Synthesis of Betulinic Acid Ester Using Artificial Neural Networks and Support Vector Machine

TL;DR

This study employs artificial neural networks and support vector machines to accurately predict the yield of betulinic acid ester synthesis, significantly reducing experimental optimization efforts.

Contribution

The paper introduces the use of GRNN and SVM models for predicting ester synthesis yields, demonstrating high accuracy and efficiency in modeling biochemical reactions.

Findings

GRNN and SVM achieved 100% prediction accuracy within 30% tolerance.

Both models had low RMS errors (~4.1) and rapid training times (1 second).

Models effectively predict yields based on reaction parameters, aiding process optimization.

Abstract

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%.