  Prediction of molecular properties, including physico-chemical properties, is
a challenging task in chemistry. Herein we present a new state-of-the-art
multitask prediction method based on existing graph neural network models. We
have used different architectures for our models and the results clearly
demonstrate that multitask learning can improve model performance.
Additionally, a significant reduction of variance in the models has been
observed. Most importantly, datasets with a small amount of data points reach
better results without the need of augmentation.
