Issue 2, 2024

Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules

Abstract

We evaluate the effectiveness of fine-tuning GPT-3 for the prediction of electronic and functional properties of organic molecules. Our findings show that fine-tuned GPT-3 can successfully identify and distinguish between chemically meaningful patterns, and discern subtle differences among them, exhibiting robust predictive performance for the prediction of molecular properties. We focus on assessing the fine-tuned models' resilience to information loss, resulting from the absence of atoms or chemical groups, and to noise that we introduce via random alterations in atomic identities. We discuss the challenges and limitations inherent to the use of GPT-3 in molecular machine-learning tasks and suggest potential directions for future research and improvements to address these issues.

Graphical abstract: Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules

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Article information

Article type
Edge Article
Submitted
31 aug 2023
Accepted
04 dec 2023
First published
05 dec 2023
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2024,15, 500-510

Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules

Z. Xie, X. Evangelopoulos, Ö. H. Omar, A. Troisi, A. I. Cooper and L. Chen, Chem. Sci., 2024, 15, 500 DOI: 10.1039/D3SC04610A

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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