AI-Driven Synthetic Route Design Incorporated with Retrosynthesis Knowledge.

ABSTRACT: Computer-aided synthesis planning (CASP) aims to assist chemists in performing retrosynthetic analysis for which they utilize their experiments, intuition, and knowledge. Recent breakthroughs in machine learning (ML) techniques, including deep neural networks, have significantly improved data-driven synthetic route designs without human intervention. However, learning chemical knowledge by ML for practical synthesis planning has not yet been adequately achieved and remains a challenging problem. In this study, we developed a data-driven CASP application integrated with various portions of retrosynthesis knowledge called "ReTReK" that introduces the knowledge as adjustable parameters into the evaluation of promising search directions. The experimental results showed that ReTReK successfully

SUBMITTER: Ishida S 

PROVIDER: S-EPMC8965881 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature