论文标题
一个新的增强学习框架，以发现自然风味分子
A new Reinforcement Learning framework to discover natural flavor molecules
论文作者
论文摘要
味道是遵循社会趋势和行为的风味行业的焦点。新调味剂和分子的研究和开发在该领域至关重要。另一方面，自然风味的发展在现代社会中起着至关重要的作用。鉴于这一点，目前的工作提出了一个基于科学机器学习的新框架，以在风味工程和行业中解决新兴问题。因此，这项工作带来了一种创新的方法来设计新的自然风味分子。评估了分子有关合成可及性，原子数以及与天然或伪自然产物的相似性的评估。
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.
