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Arxiv
1d
23
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Guided Generation for Developable Antibodies
- A computational framework for optimizing antibody sequences for favorable developability has been introduced.
- The framework includes a guided discrete diffusion model trained on natural paired heavy- and light-chain sequences from the Observed Antibody Space (OAS).
- Integration of Soft Value-based Decoding in Diffusion (SVDD) Module helps bias sampling towards biophysically viable candidates without compromising naturalness.
- The model shows significant enrichment in predicted developability scores over unguided baselines and enables the ML-driven pipeline for designing antibodies.
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