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• W3098364090 abstract "Neural methods of molecule property prediction require efficient encoding of structure and property relationship to be accurate. Recent work using graph algorithms shows limited generalization in the latent molecule encoding space. We build a Transformer-based molecule encoder and property predictor network with novel input featurization that performs significantly better than existing methods. We adapt our model to semi-supervised learning to further perform well on the limited experimental data usually available in practice." @default.
• W3098364090 created "2020-11-23" @default.
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• W3098364090 date "2020-11-05" @default.
• W3098364090 modified "2023-09-24" @default.
• W3098364090 title "Transformer Based Molecule Encoding for Property Prediction" @default.
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