FLAb: Fitness Landscapes for Antibodies with Developability Data
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Description
FLAb is the largest publicly available therapeutic antibody dataset designed for training and benchmarking protein AI models. It provides open-access, high-quality developability data on diverse therapeutic properties, including expression, thermostability, immunogenicity, aggregation, polyreactivity, binding affinity, and pharmacokinetics. The dataset was contributed by the Jeffrey Gray Lab at Johns Hopkins University and is available under a CC-BY-4.0 license.
Use Cases
Train protein language models based on diverse therapeutic property data.
Benchmark antibody developability prediction models based on features like thermostability and aggregation.
Optimize antibody candidates for clinical development based on pharmacokinetics and immunogenicity data.
Study fitness landscapes for antibody sequences based on binding affinity and expression data.
Strengths
Described as the largest publicly available therapeutic antibody dataset.
Provides high-quality data on multiple developability properties.
Released under a permissive CC-BY-4.0 license.
Limitations
Column-level documentation is absent; field semantics must be inferred after download.
Row count and dataset size are unknown, which may limit suitability assessment.
Provenance
Source
Jeffrey Gray Lab, Johns Hopkins University
Collection Method
Likely contains experimental and/or computational measurements of antibody properties.
Time Range
null
Freshness
Last update date is unknown; freshness unverified.
Geography
null
Data is hosted on AWS S3; users need appropriate tools or cloud access for download.