Ajou University repository

Towards Accurate and Certain Molecular Properties Prediction
  • Ham, Kyung Pyo ;
  • Yoon, Jeong Noh ;
  • Sael, Lee
Citations

SCOPUS

0

Citation Export

Publication Year
2022-01-01
Journal
International Conference on ICT Convergence
Publisher
IEEE Computer Society
Citation
International Conference on ICT Convergence, Vol.2022-October, pp.1621-1624
Keyword
graph neural networkmolecular property predictionuncertainty estimation
Mesh Keyword
AS graphGraph neural networksModel estimationMolecular propertiesMolecular property predictionNetwork uncertaintiesNeural network modelPrediction problemProperty predictionsUncertainty estimation
All Science Classification Codes (ASJC)
Information SystemsComputer Networks and Communications
Abstract
In this review, we survey the fundamental problems and their solvers in molecular property prediction problems: graph neural networks modeling and uncertainty estimation. Molecules are naturally represented with graphs, thus graph neural networks, such as graph convolution networks and several types of message passing networks, are their natural models for accurate predictions. However, graph neural networks and any other deep learning models require a large amount of data. Unfortunately, molecular data are insufficient and skewed. Thus, not just an accurate method but models that return uncertainty estimations, such as Bayesian estimations, deep ensembles, and evidential methods, are required.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36816
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85143254020&origin=inward
DOI
https://doi.org/10.1109/ictc55196.2022.9952716
Journal URL
http://ieeexplore.ieee.org/xpl/conferences.jsp
Type
Conference
Funding
This work was supported by the Institute of Information & Communications Technology Planning Evaluation (IITP) grant funded by the Korean government (MSIT) (No.2022-0-00369) and by the National Research Foundation of Korea Grant funded by the Korean government (2022R1F1A1065664). *KH and JY contributed equally. \u2020LS is the corresponding author.
Show full item record

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Lee, Sael Image
Lee, Sael이슬
Department of Software and Computer Engineering
Read More

Total Views & Downloads

File Download

  • There are no files associated with this item.