Efficient Exploration of Chemical Compound Space Using Active Learning for Prediction of Thermodynamic Properties of Alkane Molecules-Reference-Cited by-同舟云学术

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Efficient Exploration of Chemical Compound Space Using Active Learning for Prediction of Thermodynamic Properties of Alkane Molecules

Published:2023-10-19 Issue:21 Volume:63 Page:6515-6524
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Xiang Yan¹^ORCID,Tang Yu-Hang²³^ORCID,Gong Zheng¹,Liu Hongyi¹,Wu Liang¹^ORCID,Lin Guang⁴^ORCID,Sun Huai¹^ORCID

Affiliation:

1. School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

2. Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States

3. NVIDIA Corporation, Santa Clara, California 95051, United States

4. Department of Mathematics & School of Mechanical Engineering, Purdue University, West Lafayette, Indiana 47907, United States

Funder

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.3c01430

Reference43 articles.

1. Settles, B. Active Learning Literature Survey; Technical Report; University of Wisconsin-Madison Department of Computer Sciences, 2009. https://minds.wisconsin.edu/handle/1793/60660 (accessed July 09, 2021).

2. Information-Based Objective Functions for Active Data Selection

3. Active Learning with Statistical Models

4. Active-learning strategies in computer-assisted drug discovery

5. Practical considerations for active machine learning in drug discovery

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