PuSH - Publikationsserver des Helmholtz Zentrums München

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)
DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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.
Altmetric
Weitere Metriken?
[➜Einloggen]
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Korrespondenzautor
Schlagwörter Chemistry Informer Libraries; Chemical-reaction; Machine; Generation; Language; System; Information; Design; Smiles; Qsar
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
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Structural Biology (STB)
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