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

Understand

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.