Antibody design using deep learning: from sequence and structure design to affinity maturation

Sara Joubbi, Alessio Micheli, Paolo Milazzo, Giuseppe Maccari, Giorgio Ciano, Dario Cardamone, Duccio Medini

Briefings in Bioinformatics 2024 article cited by 50

importance 5/10 ai assisted Monoclonal and Polyclonal Antibodies Research antibody engineering stage: research

Developing antibodies is slow and costly; the paper reviews how deep learning can streamline antibody sequence/structure design, docking and affinity maturation.

Faster antibody development could speed drug discovery against complex antigens and benefit patients.

Survey of deep learning techniques combined with in vitro methods for antibody design, folding, antibody-antigen docking and affinity maturation.

not stated in source

Abstract claims computational power expedites lead candidate generation and antibody development, but gives no measured outcomes.

Venue Briefings in Bioinformatics
Published 2024
Work type article
Authors Sara Joubbi, Alessio Micheli, Paolo Milazzo, Giuseppe Maccari, Giorgio Ciano, Dario Cardamone, Duccio Medini
Citations 50 (OpenAlex, at import time)
References 194
Model or method not stated in source
Organization not stated in source
Primary source https://doi.org/10.1093/bib/bbae307
Open access gold
Keywords antibody design, deep learning, affinity maturation, docking, drug discovery
5 / 10

real but narrower advance

Review claims deep learning expedites lead candidate generation and antibody development against complex antigens, relevant to drug discovery, but provides no measured outcomes or scale.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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