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Voinarovska, V.* ; Kabeshov, M.* ; Dudenko, D.* ; Genheden, S.* ; Tetko, I.V.

When yield prediction does not yield prediction: An overview of the current challenges.

J. Chem. Inf. Model. 64, 42-56 (2024)
Publ. Version/Full Text DOI PMC
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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
Language english
Publication Year 2024
Prepublished in Year 2023
HGF-reported in Year 2023
ISSN (print) / ISBN 0021-9576
e-ISSN 1520-5142
Journal Journal of Chemical Information and Modeling
Quellenangaben Volume: 64, Issue: 1, Pages: 42-56 Article Number: , Supplement: ,
Publisher American Chemical Society (ACS)
Publishing Place 1155 16th St, Nw, Washington, Dc 20036 Usa
Reviewing status Peer reviewed
Institute(s) Institute of Structural Biology (STB)
POF-Topic(s) 30203 - Molecular Targets and Therapies
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-503000-001
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
DOI 10.1021/acs.jcim.3c01524
WOS ID 001138386200001
Scopus ID 85180976579
PubMed ID 38116926
Erfassungsdatum 2024-01-10
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