A transfer learning approach for reaction discovery in small data situations using generative model

ABSTRACT: Summary Sustainable practices in chemical sciences can be better realized by adopting interdisciplinary approaches that combine the advantages of machine learning (ML) on the initially acquired small data in reaction discovery. Developing new reactions generally remains heuristic and even time and resource intensive. For instance, synthesis of fluorine-containing compounds, which constitute ∼20% of the marketed drugs, relies on deoxyfluorination of abundantly available alcohols. Herein, we demonstrate the use of a recurrent neural network-based deep generative model built on a library of just 37 alcohols for effective learning and exploration of the chemical space. The proof-of-concept ML model is able to generate good quality, synthetically accessible, higher-yielding novel alcohol molecules. This protocol would have superior utility for deployment into a practical reaction discovery pipeline. Graphical abstract Highlights • Dual pronged transfer learning, both to generate and predict yields of new molecules• Demonstrated the utility for an important family of deoxyfluorination of alcohols• Applicable for practically more likely situations with relatively smaller data• Extendable to other reaction manifolds to facilitate expedited reaction discovery Artificial intelligence; Computational chemistry; Functional group chemistry; Modeling chemical reactivity

SUBMITTER: Singh S 

PROVIDER: S-EPMC9272387 | biostudies-literature |

REPOSITORIES: biostudies-literature