Computer‐Aided Synthesis Planning (CASP) and Machine Learning: Optimizing Chemical Reaction Conditions

Computer‐aided synthesis planning (CASP) has garnered increasing attention in light of recent advancements in machine learning models. While the focus is on reverse synthesis or forward outcome prediction, optimizing reaction conditions remains a significant challenge. For datasets with multiple var...

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Bibliographic Details
Published in:Chemistry : a European journal Vol. 30; no. 55; pp. e202401626 - n/a
Main Authors: Han, Yu, Deng, Mingjing, Liu, Ke, Chen, Jia, Wang, Yuting, Xu, Yu‐Ning, Dian, Longyang
Format: Journal Article
Language:English
Published: Germany Wiley Subscription Services, Inc 01.10.2024
Subjects:
CASP
Chemical reaction condition
Chemical reactions
Chemical synthesis
High-throughput screening
Learning algorithms
Machine learning
Chemical reaction condition
High-throughput screening
CASP
Machine learning
ISSN:0947-6539, 1521-3765, 1521-3765
Online Access:Get full text
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Summary:Computer‐aided synthesis planning (CASP) has garnered increasing attention in light of recent advancements in machine learning models. While the focus is on reverse synthesis or forward outcome prediction, optimizing reaction conditions remains a significant challenge. For datasets with multiple variables, the choice of descriptors and models is pivotal. This selection dictates the effective extraction of conditional features and the achievement of higher prediction accuracy. This review delineates the origins of data in conditional optimization, the criteria for descriptor selection, the response models, and the metrics for outcome evaluation, aiming to acquaint readers with the latest research trends and facilitate more informed research in this domain. This paper reviews the topic of the Computer Aided Synthesis Project (CASP) and gives a detailed overview of the methods of machine learning to predict chemical reaction conditions, including the problem of data and descriptor selection, the indicators for the evaluation of results and the current research progress of various algorithms. The future research trends and possible challenges in chemical condition prediction are prospected.
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ISSN:0947-6539
1521-3765
1521-3765
DOI:10.1002/chem.202401626