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Abstract: In this talk, I will explain how to empower graph neural networks (GNNs) for molecular property prediction with more expressive models and large datasets. (GNNs) have emerged as one of the most important innovations for machine learning in drug discovery. Their ability to work on unstructured data enables us to use deep learning on molecular graphs, with the promise of predicting molecular properties with the same speed and accuracy that convolutional networks process images.

Speaker: Dominique Beaini - https://twitter.com/dom_beaini 

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