Uncertainty quantification for molecular property predictions with graph neural architecture search-Reference-Cited by-同舟云学术

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Uncertainty quantification for molecular property predictions with graph neural architecture search

Published:2024 Issue:8 Volume:3 Page:1534-1553
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Jiang Shengli¹^ORCID,Qin Shiyi¹,Van Lehn Reid C.¹,Balaprakash Prasanna²,Zavala Victor M.¹³^ORCID

Affiliation:

1. Department of Chemical and Biological Engineering, University of Wisconsin–Madison, 1415 Engineering Dr, Madison, WI 53706, USA

2. Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA

3. Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL 60439, USA

Abstract

AutoGNNUQ employs neural architecture search to enhance uncertainty quantification for molecular property prediction via graph neural networks.

Funder

Advanced Scientific Computing Research

Division of Information and Intelligent Systems

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/DD/D4DD00088A

Reference60 articles.

1. QSAR Modeling: Where Have You Been? Where Are You Going To?

2. Applications of Deep Learning in Molecule Generation and Molecular Property Prediction

3. PotentialNet for Molecular Property Prediction

4. Z.Hao , C.Lu , Z.Huang , H.Wang , Z.Hu , Q.Liu , E.Chen and C.Lee , Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2020 , pp. 731–752

5. J.Gilmer , S. S.Schoenholz , P. F.Riley , O.Vinyals and G. E.Dahl , International conference on machine learning , 2017 , pp. 1263–1272

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