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In the computational prediction of chemical compound properties, moleculardescriptors and fingerprints encoded to low dimensional vectors are used. Theselection of proper molecular descriptors and fingerprints is both importantand challenging as the performance of such models is highly dependent ondescriptors. To overcome this challenge, natural language processing modelsthat utilize simplified molecular input line-entry system as input werestudied, and several transformer-variant models achieved superior results whencompared with conventional methods. In this study, we explored the structuraldifferences of the transformer-variant model and proposed a new self-attentionbased model. The representation learning performance of the self-attentionmodule was evaluated in a multi-task learning environment using imbalancedchemical datasets. The experiment results showed that our model achievedcompetitive outcomes on several benchmark datasets. The source code of ourexperiment is available at https://github.com/arwhirang/sa-mtl and the datasetis available from the same URL.

Predicting Chemical Properties using Self-Attention Multi-task Learning based on SMILES Representation