DOI: 10.1039/c9me00078j. Designing compact training sets for data-driven molecular property prediction through optimal exploitation and exploration

Designing compact training sets for data-driven molecular property prediction through optimal exploitation and exploration

10.1039/c9me00078j

Crossref journal-article

Royal Society of Chemistry (RSC)

Molecular Systems Design & Engineering (292)

Abstract

In this paper, we consider the problem of designing a compact training set comprising the most informative molecules from a specified library to build data-driven molecular property models.

Bibliography

Li, B., & Rangarajan, S. (2019). Designing compact training sets for data-driven molecular property prediction through optimal exploitation and exploration. Molecular Systems Design & Engineering, 4(5), 1048â1057.

Authors 2

Bowen Li (first)
Srinivas Rangarajan (additional)

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Dates

Type	When
Created	5 years, 11 months ago (Aug. 27, 2019, 11:05 a.m.)
Deposited	1 year ago (July 22, 2024, 12:32 p.m.)
Indexed	1 month, 1 week ago (July 8, 2025, 1:25 p.m.)
Issued	6 years, 7 months ago (Jan. 1, 2019)
Published	6 years, 7 months ago (Jan. 1, 2019)
Published Online	6 years, 7 months ago (Jan. 1, 2019)

Funders 1

Lehigh University 10.13039/100008234
Region: Americas
pri (Universities (academic only))
Labels2
1. Lehigh
2. LU

BibTeX

@article{Li_2019, title={Designing compact training sets for data-driven molecular property prediction through optimal exploitation and exploration}, volume={4}, ISSN={2058-9689}, url={http://dx.doi.org/10.1039/c9me00078j}, DOI={10.1039/c9me00078j}, number={5}, journal={Molecular Systems Design & Engineering}, publisher={Royal Society of Chemistry (RSC)}, author={Li, Bowen and Rangarajan, Srinivas}, year={2019}, pages={1048–1057} }

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