Domain knowledge, just evaluation, and robust data standards are required

Practical guide for food scientists to build AI: Data, algorithms, and applications

AI-discovered enzymes mask deoxynivalenol toxicity and halt FHB spread in wheat grains

Molecular atlas of 896 key food odorants from 2282 food samples

Biocomposites with high strength and water-resistant sealing

Mapping sleep-promoting volatiles in 991 aromatic plants

Machine learning unveils food complexity

(#Under maintenance) Your AI-powered assistant for food science research

Data resources on food-risk components

Artificial intelligence in smart seafood safety across the supply chains

Elimination of deoxynivalenol (DON) via microbial and enzymatic strategies

Data-driven elucidation of flavor chemistry

Enhanced deep learning model for carbon footprints of chemicals

Positive Unlabeled learning-based enzyme promiscuity prediction

High-throughput prediction of enzyme promiscuity based on substrate–product pairs

Coassembly of hybrid microscale biomaterials for robust and sustainable bioplastics

A comprehensive food risk component database with scaffold analysis tool

Screening potential illegal additives using molecular fingerprints and similarity

A comprehensive bioinformatics knowledge-base for food additive chemicals

A data-driven integrative platform for prediction of toxin biotransformation

Predicting chemical's activities to stimulate muscle growth via β2-adrenoceptor

A data-driven platform for investigating masked forms of mycotoxins

An AI-driven platform for next-gen sweetener discovery

Analysis of public opinion on food safety with big data and machine learning

Mapping research landscape on synthetic biology with text mining

Data-driven prediction of molecular biotransformations in food fermentation