PFAS Bioactivity Classification for 2,649 Substances via QSAR and Molecular Docking

2,649 per- and polyfluoroalkyl substances (PFASs) classified for bioactivity potential using computational models. The dataset was created by Wenting Li and published on figshare in April 2026. It integrates quantitative structure–activity relationship (QSAR) models and molecular docking binding scores for PFAS interactions with six health-relevant proteins.

Use Cases

• Predicting chemical bioactivity based on molecular docking binding scores to six proteins.
• Training QSAR models using the 44 chemical descriptors mentioned in the description.
• Analyzing structure-activity relationships for PFASs, focusing on features like molecular complexity and hetero ring count.
• Screening PFASs for potential toxicity based on interactions with human serum albumin and peroxisome proliferator-activated receptor γ.

Strengths

• Includes binding scores for 4,397 PFASs simulated across 12 ligand-binding sites.
• Training data incorporates binary bioactivity classifications for 560 PFASs from public databases.
• Models integrate 44 chemical descriptors for each target protein.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for the primary classified set of 2,649 PFASs is unknown, which may limit suitability assessment.

Provenance

Source

figshare

Collection Method

Data generated via multicondition QSAR modeling and molecular docking simulations.

Time Range

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Freshness

Last updated 2026-04-09 21:29:29; freshness should be verified.

Geography

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