A focus on molecular representation learning for the prediction of chemical properties-Reference-Cited by-同舟云学术

A focus on molecular representation learning for the prediction of chemical properties

Published:2024 Issue:14 Volume:15 Page:5052-5055
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Harnik Yonatan¹^ORCID,Milo Anat¹^ORCID

Affiliation:

1. Department of Chemistry, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel

Abstract

Molecular representation learning (MRL) holds significant potential for predicting diverse chemical properties. In this focus article, we will provide context for applications of MRL in chemistry and the significance of King-Smith's recently published work within this evolving field.

Funder

Israel Science Foundation

Kreitman School of Advanced Graduate Studies, Ben-Gurion University of the Negev

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/SC/D4SC90043J

Reference37 articles.

1. V.Consonni and R.Todeschini , Molecular descriptors. Challenges and Advances in Computational Chemistry and Physics , 2010 , vol. 8 , pp. 29–102

2. Molecular Machine Learning for Chemical Catalysis: Prospects and Challenges

3. Representation Learning: A Review and New Perspectives

4. Self-Supervised Representation Learning: Introduction, advances, and challenges

5. Deep learning methods for molecular representation and property prediction

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