AstraZeneca/DiffAbXL
The official implementation of DiffAbXL benchmarked in the paper "Exploring Log-Likelihood Scores for Ranking Antibody Sequence Designs", formerly titled "Benchmarking Generative Models for Antibody Design".
Implements diffusion-based generative models for antibody sequence design with two operational modes—de novo generation and structure-guided design—evaluated via log-likelihood scoring for ranking sequences by predicted binding affinity. The framework benchmarks multiple model architectures (diffusion, LLM, and graph-based) across five experimental datasets with standardized interfaces, enabling direct comparison of ranking performance through Spearman correlation metrics. Built on PyTorch with configurable training pipelines and provides modular integration points for external models via standardized benchmark interfaces.
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Language
Python
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Apache-2.0
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Last pushed
Jun 11, 2025
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