Data-Driven Elucidation of Flavor Chemistry

Abstract

Flavor molecules are commonly used in the food industry to enhance product quality and consumer experiences but are associated with potential human health risks, highlighting the need for safer alternatives. To address these health-associated challenges and promote reasonable application, several databases for flavor molecules have been constructed. However, no existing studies have comprehensively summarized these data resources according to quality, focused fields, and potential gaps. Here, we systematically summarized 25 flavor molecule databases published within the last 20 years and revealed that data inaccessibility, untimely updates, and nonstandard flavor descriptions are the main limitations of current studies. We examined the development of computational approaches (e.g., machine learning and molecular simulation) for the identification of novel flavor molecules and discussed their major challenges regarding throughput, model interpretability, and the lack of gold-standard data sets for equitable model evaluation. Additionally, we discussed future strategies for the mining and designing of novel flavor molecules based on multi-omics and artificial intelligence to provide a new foundation for flavor science research.

Keywords: active ingredients; bioinformatics; cheminformatics; database; machine learning.

Figure 1

Data-driven study in flavor science. Flavor molecules in perfumes, herbs, and foods are responsible for the stimulation of human sensory perceptions. Owing to the increasing number of known flavor molecules, specialized food molecule databases were built based on data management software, such as MySQL and PostgreSQL. These databases enabled the application of computational strategies (e.g., machine learning and molecular simulation) in flavor science and, in conjunction with sensory analysis, have been successfully used to identify novel flavor molecules. With the rapid development of multi-omics and artificial intelligence, advanced computational approaches have expressed great potential in guiding the designing of artificial flavorings and the mining of natural flavor molecules.

Figure 2

Overview of flavor databases released from 1998 to 2022. Databases containing taste molecules are presented at the top of the timeline, and databases containing aroma molecules are at the bottom. The colors represent the types of databases.

Figure 3

Keyword co-occurrence network of the literature related to (A) flavor databases and (B) flavor molecule identification. Each circle in the diagram represents a unique keyword. Circle size indicates the number of keyword occurrences in the literature. The color gradient from blue to yellow corresponds to the timeline (bottom right).

Figure 4

Schematics of computational strategies for mining novel flavor molecules. (A) Schematic of molecular simulation, including data preparation, simulation, result analysis, and experimental validation. (B) Schematic of molecular machine learning, including data set preparation, modeling, result analysis, and screening.

Figure 5

Future strategies for mining natural flavor molecules and designing artificial alternatives. (A) Schematic of mining natural flavor molecules based on multi-omics. Based on plant genome and metabolome data, novel natural products are annotated using software, such as plantSMASH and NPLinker. Machine learning models could subsequently be used to predict the flavor characteristics of these natural products to discover novel natural flavor molecules. (B) Design of artificial flavor molecules based on molecular generation. By identifying molecular presentations (e.g., string-based and molecular graphs) and functions that map a set of properties to a group of molecular structures, generative models could be used to rapidly identify diverse sets of molecules highly optimized for flavor characteristics. Note: SMILES, simplified molecular-input line-entry system.