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In drug-discovery-related tasks such as virtual screening, machine learningis emerging as a promising way to predict molecular properties. Conventionally,molecular fingerprints (numerical representations of molecules) are calculatedthrough rule-based algorithms that map molecules to a sparse discrete space.However, these algorithms perform poorly for shallow prediction models or smalldatasets. To address this issue, we present SMILES Transformer. Inspired byTransformer and pre-trained language models from natural language processing,SMILES Transformer learns molecular fingerprints through unsupervisedpre-training of the sequence-to-sequence language model using a huge corpus ofSMILES, a text representation system for molecules. We performed benchmarks on10 datasets against existing fingerprints and graph-based methods anddemonstrated the superiority of the proposed algorithms in small-data settingswhere pre-training facilitated good generalization. Moreover, we define a novelmetric to concurrently measure model accuracy and data efficiency.

SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery