Size doesn't matter: predicting physico- or biochemical properties based on dozens of molecules

TL;DR

This paper demonstrates that transfer learning with graph convolutional neural networks significantly improves the prediction of chemical properties for small molecules, especially when data is limited.

Contribution

It introduces a transfer learning approach using graph convolutional neural networks for small molecules, enhancing model performance with limited data.

Findings

Transfer learning improves model accuracy on small datasets.

Dataset composition affects model quality and applicability.

Graph convolutional neural networks effectively capture chemical features.

Abstract

The use of machine learning in chemistry has become a common practice. At the same time, despite the success of modern machine learning methods, the lack of data limits their use. Using a transfer learning methodology can help solve this problem. This methodology assumes that a model built on a sufficient amount of data captures general features of the chemical compound structure on which it was trained and that the further reuse of these features on a dataset with a lack of data will greatly improve the quality of the new model. In this paper, we develop this approach for small organic molecules, implementing transfer learning with graph convolutional neural networks. The paper shows a significant improvement in the performance of models for target properties with a lack of data. The effects of the dataset composition on model quality and the applicability domain of the resulting models are also considered.