Inverse design of chemoenzymatic epoxidation of soyabean oil through artificial intelligence-driven experimental approach.

This paper presents an inverse design methodology that utilizes artificial intelligence (AI)-driven experiments to optimize the chemoenzymatic epoxidation of soyabean oil using hydrogen peroxide and lipase (Novozym 435). First, experiments are conducted using a systematic 3-level, 5-factor Box-Behnken design to explore the effect of input parameters on oxirane oxygen content (OOC (%)). Based on these experiments, various AI models are trained, with the support vector regression (SVR) model being found to be the most accurate.

Machine learning aided experimental approach for evaluating the growth kinetics of Candida antarctica for lipase production.

A hybrid machine learning (ML) aided experimental approach was proposed in this study to evaluate the growth kinetics of Candida antarctica for lipase production. Different ML models were trained and optimized to predict the growth curves at various substrate concentrations. Results on comparison demonstrate the superior performance of the Gradient boosting regression (GBR) model in growth curves prediction.

Recent advances on sources and industrial applications of lipases.

Lipases are the industrially important biocatalysts, which are envisioned to have tremendous applications in the manufacture of a wide range of products. Their unique properties such as better stability, selectivity and substrate specificity position them as the most expansively used industrial enzymes. The research on production and applications of lipases is ever growing and there exists a need to have a latest review on the research findings of lipases.