CS-DTA: Drug-Target Affinity Predictions for Cold-Start Scenarios

A 720.4 KB document describing CS-DTA, a language model-driven framework for predicting drug-target affinity under strict cold-start conditions. The framework was developed by Zhaokun Jiang and integrates large language models for compound and protein representation learning with a cross-modal interaction module. The associated data was last updated on 2026-04-28.

Use Cases

• Benchmarking new drug-target affinity prediction models based on the described cold-start scenarios.
• Training or validating models that leverage semantic patterns from molecular and protein sequences as described in the framework.
• Conducting interpretability analyses on binding interactions based on the highlighted ligand substructures and protein regions.
• Prioritizing target-specific interactions for downstream screening and molecular docking validation as discussed in the results.

Strengths

• The framework achieved state-of-the-art predictive performance in both warm-start and strict cold-start scenarios as reported.
• The description provides detailed methodological insight into the integration of LLM-based encoders and a cross-modal attention mechanism.
• The document is shared under a permissive CC-BY-4.0 license, allowing for broad reuse.

Limitations

• The dataset's row count and specific column structure are unknown, limiting suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The primary file format is DOCX, which may require conversion for direct computational use.

Provenance

Source

figshare

Collection Method

Likely contains results and methodological descriptions from the CS-DTA research framework.

Freshness

Last updated 2026-04-28 05:24:19; freshness should be verified.