# A simple DNN regression for the chemical composition in essential oil

arXiv: https://arxiv.org/abs/2412.12936 · HTML: https://mlsanity.com/paper/2412.12936/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 17 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1510.03820/) (2015)
- [Olfactory Label Prediction on Aroma-Chemical Pairs](https://mlsanity.com/paper/2312.16124/) (2023)

## Built on by (most cited first, 1 of 1)

- [Motional representation; the ability to predict odor characters using molecular vibrations](https://mlsanity.com/paper/2509.16245/) (2025)

## Abstract (arXiv)

Although experimental design and methodological surveys for mono-molecular activity/property has been extensively investigated, those for chemical composition have received little attention, with the exception of a few prior studies. In this study, we configured three simple DNN regressors to predict essential oil property based on chemical composition. Despite showing overfitting due to the small size of dataset, all models were trained effectively in this study.