Augmented Canonical Druglike QED Pfizer 15M: Molecular SMILES Dataset

Derify's dataset contains molecular structures for drug discovery, derived from the Druglike molecule datasets. The data was canonicalized using RDKit (2024.9.4) for structural consistency, and 33% of the dataset was randomly sampled and augmented using RDKit's Chem.MolToRandomSmilesVect function to enhance diversity. The dataset was last updated on September 9, 2025.

Use Cases

• Train generative models for novel molecule design based on the augmented SMILES representations.
• Benchmark molecular property prediction models using the canonicalized and augmented structural data.
• Study the impact of SMILES augmentation techniques on model performance and molecular diversity.

Strengths

• Dataset was canonicalized using RDKit (2024.9.4) to ensure structural consistency.
• 33% of the dataset was randomly sampled and augmented using RDKit's Chem.MolToRandomSmilesVect function to enhance molecular diversity.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Derived from the Druglike molecule datasets for drug discovery.

Collection Method

Canonicalized using RDKit and augmented via random sampling and RDKit's Chem.MolToRandomSmilesVect function.

Freshness

Last updated 2025-09-09 22:33:31.