Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery

Прогностическая химия: машинное обучение для внедрения реакций, разработки реакций и открытия реакций
Connor W. Coley, Zhengkai Tu, Thijs Stuyver
2022-11-28

computer-aided retrosynthesismachine learning for reaction deploymentpredictive chemistryreaction developmentreaction discovery
The field of predictive chemistry relates to the development of models able to describe how molecules interact and react. It encompasses the long-standing task of computer-aided retrosynthesis, but is far more reaching and ambitious in its goals. In this review, we summarize several areas where predictive chemistry models hold the potential to accelerate the deployment, development, and discovery of organic reactions and advance synthetic chemistry.
1
Predictive chemistry can facilitate discovery of new organic reactions.
2
Predictive chemistry can speed development of organic reactions.
3
Predictive chemistry has potential to accelerate deployment of organic reactions.
4
Predictive chemistry models aim to describe how molecules interact and react, extending beyond retrosynthesis.
5
The review summarizes multiple areas where predictive chemistry models can advance synthetic chemistry.

Predictive chemistry models for organic reactions

Use of machine learning models to describe, predict, and accelerate deployment, development, and discovery of organic reaction behavior and outcomes

Publication Details
Publication Date
2022-11-28
Journal
Publisher
ISSN
Cited by
147
Access Type
Author Information
Authors
Connor W. Coley
Zhengkai Tu
Thijs Stuyver
Explore further
Open the scid.ai AI chat with a ready-made request: it will find papers on a similar topic and help build a literature review.
Find similar papers in the chat
Make a presentation
100%