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Molecular property prediction is essential in chemistry, especially for drugdiscovery applications. However, available molecular property data is oftenlimited, encouraging the transfer of information from related data. Transferlearning has had a tremendous impact in fields like Computer Vision and NaturalLanguage Processing signaling for its potential in molecular propertyprediction. We present a pre-training procedure for molecular representationlearning using reaction data and use it to pre-train a SMILES Transformer. Wefine-tune and evaluate the pre-trained model on 12 molecular propertyprediction tasks from MoleculeNet within physical chemistry, biophysics, andphysiology and show a statistically significant positive effect on 5 of the 12tasks compared to a non-pre-trained baseline model.

Pre-training Transformers for Molecular Property Prediction Using Reaction Prediction