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When yield prediction does not yield prediction: An overview of the current challenges.
J. Chem. Inf. Model. 64, 42-56 (2024)
Machine Learning (ML) techniques face significant challenges when predicting advanced chemical properties, such as yield, feasibility of chemical synthesis, and optimal reaction conditions. These challenges stem from the high-dimensional nature of the prediction task and the myriad essential variables involved, ranging from reactants and reagents to catalysts, temperature, and purification processes. Successfully developing a reliable predictive model not only holds the potential for optimizing high-throughput experiments but can also elevate existing retrosynthetic predictive approaches and bolster a plethora of applications within the field. In this review, we systematically evaluate the efficacy of current ML methodologies in chemoinformatics, shedding light on their milestones and inherent limitations. Additionally, a detailed examination of a representative case study provides insights into the prevailing issues related to data availability and transferability in the discipline.
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Publication type
Article: Journal article
Document type
Review
Keywords
Chemistry Informer Libraries; Chemical-reaction; Machine; Generation; Language; System; Information; Design; Smiles; Qsar
ISSN (print) / ISBN
0021-9576
e-ISSN
1520-5142
Quellenangaben
Volume: 64,
Issue: 1,
Pages: 42-56
Publisher
American Chemical Society (ACS)
Publishing Place
1155 16th St, Nw, Washington, Dc 20036 Usa
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Structural Biology (STB)
Grants
odowska-Curie Actions grant agreement "Advanced Machine Learning for Innovative Drug Discovery
European Union's Horizon 2020 research and innovation program under the Marie Sklstrok
HORIZON EUROPE Marie Sklodowska-Curie Actions
European Union's Horizon 2020 research and innovation program under the Marie Sklstrok
HORIZON EUROPE Marie Sklodowska-Curie Actions