Deep Learning Approaches for Enhancing Bioavailability Predictions of Functional Food Ingredients

Abstract

Bioavailability of functional food components is essential to evaluate the effectiveness of these molecules in human physiology. Traditional approaches to measure bioavailability like in vitro and in vivo systems are time-consuming, costly, and of poor scalability. With the emergence of recent advances in deep learning, efficient computational models that can accurately predict the bioavailability of functional molecules based on their molecular structure, physicochemical characteristics, and multi-omics information are accessible. The research in this paper presents the integration of deep learning techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and generative adversarial networks (GANs) to enhance bioavailability predictions. The paper also considers significant datasets, feature engineering techniques, and the application of AI-based models in designing for maximum absorption and metabolism. With deep learning, it is possible to design quicker, customized, and reproducible functional food ingredient analysis. The paper sees challenges in terms of limited data, model interpretability, and regulatory, indicating potential future studies using AI-enabled bioavailability modeling.