Cancer Targets with ChEMBL and UniProt IDs for Bioactivity Prediction

Raúl Acosta-Murillo's dataset describes identified cancer targets with ChEMBL and UniProt identifiers. The data is stored in a 9.5 KB Excel file and was last updated on March 17, 2026. Platform tags suggest the dataset was used for predicting cancer bioactivities using various chemical representations and machine learning models.

Use Cases

• Predicting cancer bioactivities based on chemical representation features mentioned in the description.
• Mapping cancer targets to standardized ChEMBL and UniProt identifiers for database integration.
• Training machine learning models like Ridge or Lasso regression using features such as Morgan fingerprints or ECFP.
• Comparing the performance of different chemical fingerprint methods (e.g., Morgan, RDKit, AtomPair) for cancer target prediction.

Strengths

• Dataset is small (9.5 KB), facilitating quick download and inspection.
• Includes standardized biological identifiers (ChEMBL and UniProt IDs) for target mapping.
• License is CC-BY-4.0, permitting broad reuse with attribution.
• Platform tags indicate a clear application context for predicting cancer bioactivities.

Limitations

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

Provenance

Source

Raúl Acosta-Murillo via figshare.

Collection Method

Method of gathering is not described.

Time Range

Temporal coverage is not specified.

Freshness

Last updated 2026 03 17 17 36 08; freshness should be verified.

Geography

Spatial coverage is not specified.