De novo generation of SARS-CoV-2 antibody CDRH3 with a pre-trained generative large language model
Artificial Intelligence (AI) techniques have made great advances in assisting antibody
design. However, antibody design still heavily relies on isolating antigen-specific antibodies …
design. However, antibody design still heavily relies on isolating antigen-specific antibodies …
Linguistics-based formalization of the antibody language as a basis for antibody language models
Apparent parallels between natural language and antibody sequences have led to a surge
in deep language models applied to antibody sequences for predicting cognate antigen …
in deep language models applied to antibody sequences for predicting cognate antigen …
Accurate prediction of antibody function and structure using bio-inspired antibody language model
In recent decades, antibodies have emerged as indispensable therapeutics for combating
diseases, particularly viral infections. However, their development has been hindered by …
diseases, particularly viral infections. However, their development has been hindered by …
[HTML][HTML] Do domain-specific protein language models outperform general models on immunology-related tasks?
N Deutschmann, A Pelissier, A Weber, S Gao… - ImmunoInformatics, 2024 - Elsevier
Deciphering the antigen recognition capabilities by T-cell and B-cell receptors (antibodies)
is essential for advancing our understanding of adaptive immune system responses. In …
is essential for advancing our understanding of adaptive immune system responses. In …
Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability
Designing effective monoclonal antibody (mAb) therapeutics faces a multi-parameter
optimization challenge known as “developability”, which reflects an antibody's ability to …
optimization challenge known as “developability”, which reflects an antibody's ability to …
Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability
Designing effective monoclonal antibody (mAb) therapeutics face a significant challenge
known as" developability", which reflects an antibody's ability to progress through …
known as" developability", which reflects an antibody's ability to progress through …
Protein language models enable prediction of polyreactivity of monospecific, bispecific, and heavy-chain-only antibodies
X Yu, K Vangjeli, A Prakash, M Chhaya… - Antibody …, 2024 - academic.oup.com
Background Early assessment of antibody off-target binding is essential for mitigating
developability risks such as fast clearance, reduced efficacy, toxicity, and immunogenicity …
developability risks such as fast clearance, reduced efficacy, toxicity, and immunogenicity …
Training data composition determines machine learning generalization and biological rule discovery
Supervised machine learning models rely on training datasets with positive (target class)
and negative examples. Therefore, the composition of the training dataset has a direct …
and negative examples. Therefore, the composition of the training dataset has a direct …
[PDF][PDF] On Interpretable Deep Learning and Protein Lan-guage Models for Rational Antibody Design
A Pelissier, MR Martinez - aurelienpelissier.com
The ability of antibodies to bind to a wide variety of targets in a highly specific and selective
manner has led to an increasing interest in their use as therapeutics for a broad range of …
manner has led to an increasing interest in their use as therapeutics for a broad range of …