Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining.

ABSTRACT: Machine learning (ML) methods have great potential to transform chemical discovery by accelerating the exploration of chemical space and drawing scientific insights from data. However, modern chemical reaction ML models, such as those based on graph neural networks (GNNs), must be trained on a large amount of labelled data in order to avoid overfitting the data and thus possessing low accuracy and transferability. In this work, we propose a strategy to leverage unlabelled data to learn accurate ML models for small labelled chemical reaction data. We focus on an old and prominent problem-classifying reactions into distinct families-and build a GNN model for this task. We first pretrain the model on unlabelled reaction data using unsupervised contrastive learning and then fine-tune it on a s

SUBMITTER: Wen M 

PROVIDER: S-EPMC8809395 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature