A new Reinforcement Learning framework to discover natural flavor molecules
This work addresses the need for innovative natural flavor development in the flavor industry, though it appears incremental as it applies existing methods to a specific domain.
The paper tackles the problem of designing new natural flavor molecules by proposing a novel reinforcement learning framework, which evaluates molecules based on synthetic accessibility, atom count, and likeness to natural products.
The flavor is the focal point in the flavor industry, which follows social tendencies and behaviors. The research and development of new flavoring agents and molecules are essential in this field. On the other hand, the development of natural flavors plays a critical role in modern society. In light of this, the present work proposes a novel framework based on Scientific Machine Learning to undertake an emerging problem in flavor engineering and industry. Therefore, this work brings an innovative methodology to design new natural flavor molecules. The molecules are evaluated regarding the synthetic accessibility, the number of atoms, and the likeness to a natural or pseudo-natural product.