Transfer Learning Approach to Develop Natural Molecules with Specific Flavor Requirements

Abstract

In the past decades, the flavor industry’s investment in research and development has increased to take innovative steps. Meanwhile, the lack of information regarding the flavored molecules and specific flavoring properties is an obstacle to advances in this sector. In this context, this work presents the implementation of three scientific machine learning techniques as an innovative methodology to design new natural flavor molecules with specific desired properties to product development. The transfer learning technique is presented to tackle the lack of data available when analyzing flavor molecules. Nine flavor descriptors were studied along this work, and all of them presented more than 50% of molecules generated within the outstanding results considered for the evaluation metric, natural product-likeness score and synthetic accessibility score. Finally, a discussion of the results is constructed based on the data availability, the presence in nature, and the multisensorial flavor component impact for the specific flavors’ results.