Multitask Learning On Graph Neural Networks Applied To Molecular Property Predictions
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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 methods. 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 have been observed. Most importantly, datasets with a small amount of data points reach better results without the need of augmentation. The improvement is an effect of data augmentation.
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