Computational and artificial intelligence-based methods for antibody development
Due to their high target specificity and binding affinity, therapeutic antibodies are currently
the largest class of biotherapeutics. The traditional largely empirical antibody development …
the largest class of biotherapeutics. The traditional largely empirical antibody development …
Efficient evolution of human antibodies from general protein language models
Natural evolution must explore a vast landscape of possible sequences for desirable yet
rare mutations, suggesting that learning from natural evolutionary strategies could guide …
rare mutations, suggesting that learning from natural evolutionary strategies could guide …
AbDiffuser: full-atom generation of in-vitro functioning antibodies
K Martinkus, J Ludwiczak, WC Liang… - Advances in …, 2024 - proceedings.neurips.cc
We introduce AbDiffuser, an equivariant and physics-informed diffusion model for the joint
generation of antibody 3D structures and sequences. AbDiffuser is built on top of a new …
generation of antibody 3D structures and sequences. AbDiffuser is built on top of a new …
Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space
EK Makowski, PC Kinnunen, J Huang, L Wu… - Nature …, 2022 - nature.com
Therapeutic antibody development requires selection and engineering of molecules with
high affinity and other drug-like biophysical properties. Co-optimization of multiple antibody …
high affinity and other drug-like biophysical properties. Co-optimization of multiple antibody …
Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain
The continual evolution of SARS-CoV-2 and the emergence of variants that show resistance
to vaccines and neutralizing antibodies threaten to prolong the COVID-19 pandemic …
to vaccines and neutralizing antibodies threaten to prolong the COVID-19 pandemic …
[HTML][HTML] Accelerated rational PROTAC design via deep learning and molecular simulations
Proteolysis-targeting chimeras (PROTACs) have emerged as effective tools to selectively
degrade disease-related proteins by using the ubiquitin-proteasome system. Developing …
degrade disease-related proteins by using the ubiquitin-proteasome system. Developing …
Machine learning for functional protein design
Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and
structure data have radically transformed computational protein design. New methods …
structure data have radically transformed computational protein design. New methods …
Deciphering the language of antibodies using self-supervised learning
An individual's B cell receptor (BCR) repertoire encodes information about past immune
responses and potential for future disease protection. Deciphering the information stored in …
responses and potential for future disease protection. Deciphering the information stored in …
Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies
Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs)
are tremendous, the design and discovery of new candidates remain a time and cost …
are tremendous, the design and discovery of new candidates remain a time and cost …