Self-Supervised Learning for Molecular Property Prediction

Predicting molecular properties remains a challenging task with numerous potential applications, notably in drug discovery. Recently, the development of deep learning, combined with rising amounts of data, has provided powerful tools to build predictive models. Since molecules can be encoded as graphs, Graph Neural Networks (GNNs) have emerged as a popular choice of architecture to tackle this task. Training GNNs to predict molecular properties however faces the challenge of collecting annotated data which is a costly and time consuming process. On the other hand, it is easy to access large databases of molecules without annotations.