LC-TEAD01: Covalent TEAD Inhibitor Discovery Data

A dataset from a study integrating structure-based design with CuAAC-enabled parallel synthesis to build a covalent, hydrophobic-fragment library for discovering TEAD inhibitors. The dataset, authored by Yuhui Miao and last updated on 2026-05-05, likely contains results from in situ screening which identified hits at an 8.33% rate, including the selective inhibitor LC-TEAD01. The data supports biochemical and structural studies confirming covalent engagement and in vivo tumor growth inhibition in NF2-deficient xenografts.

Use Cases

• Training predictive models for covalent inhibitor activity based on structural design and screening data
• Analyzing structure-activity relationships for TEAD inhibitors based on the described hydrophobic-fragment library
• Benchmarking novel drug discovery strategies that integrate parallel synthesis with in situ screening

Strengths

• Dataset is associated with a specific, validated hit compound (LC-TEAD01) showing a 17-fold preference for NF2-deficient cells
• The underlying study methodology is described in detail, integrating structure-based design, parallel synthesis, and in situ screening
• The dataset is small (1.6 KB), facilitating quick download and initial inspection

Limitations

• Row count is unknown, which may limit suitability assessment for machine learning
• Column-level documentation is absent; field semantics must be inferred after download
• The dataset's 1.6 KB size suggests it contains summary or key results, not the full experimental raw data

Provenance

Source

figshare, author Yuhui Miao

Collection Method

Generated from a study leveraging integrated strategic design and CuAAC-enabled parallel synthesis.

Freshness

Last updated 2026-05-05 17:37:25; freshness should be verified