Yijia Zhang¹ and Ivan Revilla², ¹Huaai Preparatory Academy, USA, ²California State Polytechnic University, USA

This paper addresses the challenge of optimizing enzyme activity and production in various industries by leveraging machine learning models [9]. Traditionally, enzyme optimization has been resource-intensive and costly [10]. Our proposed solution involves collecting diverse enzymatic reaction data, generating synthetic data, and using cross-validation and ensemble methods for model selection. Challenges such as data availability and negative value generation in dummy data were addressed creatively. Experimentation revealed that ensemble methods like Random Forest and Decision Tree Regressor outperformed linear models, highlighting the potential of machine learning in enzyme optimization [11]. This research offers a data-driven approach that promises efficiency and resource conservation, with significant implications for biotechnologists, industrial manufacturers, and the scientific community [12]. The application of machine learning in enzyme optimization not only streamlines processes but also paves the way for sustainability and innovation in enzyme-related industries, making it a compelling solution for widespread adoption.

Enzyme Optimization, Machine Learning Models, Resource Conservation, Industrial Efficiency