Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery
Прогностическая химия: машинное обучение для внедрения реакций, разработки реакций и открытия реакций
2022-11-28
SCID: 54.1/ds8dhd8q
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computer-aided retrosynthesismachine learning for reaction deploymentpredictive chemistryreaction developmentreaction discovery
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Abstract (AI)
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.
Key Findings
1
Predictive chemistry can facilitate discovery of new organic reactions.
2
Predictive chemistry can speed development of organic reactions.
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Predictive chemistry has potential to accelerate deployment of organic reactions.
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Predictive chemistry models aim to describe how molecules interact and react, extending beyond retrosynthesis.
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The review summarizes multiple areas where predictive chemistry models can advance synthetic chemistry.
Research Object
Predictive chemistry models for organic reactions
Research Subject
Use of machine learning models to describe, predict, and accelerate deployment, development, and discovery of organic reaction behavior and outcomes
Publication Details
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2022-11-28
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