Introduces TasteBench, a multimodal benchmark and competition for sensory prediction across 21K+ human evaluations on 215 plant-based foods and 15K flavor molecules, providing computational proxies to accelerate sustainable food discovery.

Couples deep learning turnover number predictions with stochastic simulated annealing to parameterize enzyme-constrained genome-scale models across 93 organisms, resolving long-standing biocatalytic data sparsity.

Crowdsources predictive models across 26 international teams in the DREAM Olfactory Challenge, demonstrating that compact semantic representations of single odorants generalize to complex mixture perceptual spaces.

Comprehensive review articulating an AI-driven framework for sustainable food formulation, spanning perception modeling, multi-omics fermentation engineering, and consumer acceptance.