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arXiv · 1911.04738

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

Shion Honda·Shoi Shi·Hiroki R. Ueda
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Abstract

In drug-discovery-related tasks such as virtual screening, machine learning is emerging as a promising way to predict molecular properties. Conventionally, molecular fingerprints (numerical representations of molecules) are calculated through rule-based algorithms that map molecules to a sparse discrete space. However, these algorithms perform poorly for shallow prediction models or small datasets. To address this issue, we present SMILES Transformer. Inspired by Transformer and pre-trained language models from natural language processing, SMILES Transformer learns molecular fingerprints through unsupervised pre-training of the sequence-to-sequence language model using a huge corpus of SMILES, a text representation system for molecules. We performed benchmarks on 10 datasets against existing fingerprints and graph-based methods and demonstrated the superiority of the proposed algorithms in small-data settings where pre-training facilitated good generalization. Moreover, we define a novel metric to concurrently measure model accuracy and data efficiency.

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Shion Honda, Shoi Shi, Hiroki R. Ueda. 2019-11-12. SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery. https://arxiv.org/abs/1911.04738

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