PuSH - Publikationsserver des Helmholtz Zentrums München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
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)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
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.
Impact Factor
Scopus SNIP
Altmetric
5.600
0.000
Tags
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern

Zusatzinfos bearbeiten
Eigene Tags bearbeiten
Privat
Eigene Anmerkung bearbeiten
Privat
Auf Publikationslisten für
Homepage nicht anzeigen
Als besondere Publikation
markieren
Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Schlagwörter Chemistry Informer Libraries; Chemical-reaction; Machine; Generation; Language; System; Information; Design; Smiles; Qsar
Sprache englisch
Veröffentlichungsjahr 2024
Prepublished im Jahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 0021-9576
e-ISSN 1520-5142
Zeitschrift Journal of Chemical Information and Modeling
Quellenangaben Band: 64, Heft: 1, Seiten: 42-56 Artikelnummer: , Supplement: ,
Verlag American Chemical Society (ACS)
Verlagsort 1155 16th St, Nw, Washington, Dc 20036 Usa
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Structural Biology (STB)
POF Topic(s) 30203 - Molecular Targets and Therapies
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503000-001
Förderungen 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
[➜Korrektur melden]