Transfer Learning Approach to Develop Natural Molecules with Specific Flavor Requirements

In the past decades, the flavor industry's investmentinresearch and development has increased to take innovative steps. Meanwhile,the lack of information regarding the flavored molecules and specificflavoring properties is an obstacle to advances in this sector. Inthis context, this work presents the implementation of three scientificmachine learning techniques as an innovative methodology to designnew natural flavor molecules with specific desired properties to productdevelopment. The transfer learning technique is presented to tacklethe lack of data available when analyzing flavor molecules. Nine flavordescriptors were studied along this work, and all of them presentedmore than 50% of molecules generated within the outstanding resultsconsidered for the evaluation metric, natural product-likeness scoreand synthetic accessibility score. Finally, a discussion of the resultsis constructed based on the data availability, the presence in nature,and the multisensorial flavor component impact for the specific flavors'results.

Automated summary

In the past decades, the flavor industry has increased its investment in research and development to take innovative steps. However, the lack of information about flavored molecules and specific flavoring properties hinders progress in this sector. This study presents the use of three scientific machine learning techniques as an innovative methodology to design new natural flavor molecules with specific desired properties for product development. The transfer learning technique is utilized to address the lack of available data when analyzing flavor molecules. Nine flavor descriptors were studied, with more than 50% of molecules generated achieving outstanding results in evaluation metrics such as natural product-likeness score and synthetic accessibility score. Finally, a discussion on the results considers data availability, presence in nature, and the impact of multisensory flavor components on specific flavor outcomes.