AIMC Topic: Antibody Affinity
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AlphaBind, a domain-specific model to predict and optimize antibody-antigen binding affinity.
mAbs
Jul 22, 2025
Antibodies are versatile therapeutic molecules that use combinatorial sequence diversity to cover a vast fitness landscape. Designing optimal antibody sequences, however, remains a major challenge. Recent advances in deep learning provide opportuniti...
Discovery and affinity maturation of antibody fragments from an unfavorably enriched phage display selection by deep sequencing and machine learning.
Journal of bioscience and bioengineering
Jun 4, 2025
Phage display selection has been used for directed evolution of antibody fragments. However, variants with binding affinity cannot be always identified due to undesirable enrichment of target-unrelated variants in the biopanning process. Here, our go...
ANTIPASTI: Interpretable prediction of antibody binding affinity exploiting normal modes and deep learning.
Structure (London, England : 1993)
Oct 25, 2024
The high binding affinity of antibodies toward their cognate targets is key to eliciting effective immune responses, as well as to the use of antibodies as research and therapeutic tools. Here, we propose ANTIPASTI, a convolutional neural network mod...
Pretrainable geometric graph neural network for antibody affinity maturation.
Nature communications
Sep 6, 2024
Increasing the binding affinity of an antibody to its target antigen is a crucial task in antibody therapeutics development. This paper presents a pretrainable geometric graph neural network, GearBind, and explores its potential in in silico affinity...
Evaluating the chaos game representation of proteins for applications in machine learning models: prediction of antibody affinity and specificity as a case study.
Journal of molecular modeling
Nov 15, 2023
CONTEXT: Machine learning techniques are becoming increasingly important in the selection and optimization of therapeutic molecules, as well as for the selection of formulation components and the prediction of long-term stability. Compared to first-p...
DG-Affinity: predicting antigen-antibody affinity with language models from sequences.
BMC bioinformatics
Nov 13, 2023
BACKGROUND: Antibody-mediated immune responses play a crucial role in the immune defense of human body. The evolution of bioengineering has led the progress of antibody-derived drugs, showing promising efficacy in cancer and autoimmune disease therap...
Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space.
Nature communications
Jul 1, 2022
Therapeutic antibody development requires selection and engineering of molecules with high affinity and other drug-like biophysical properties. Co-optimization of multiple antibody properties remains a difficult and time-consuming process that impede...
Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning.
Nature biomedical engineering
Apr 15, 2021
The optimization of therapeutic antibodies is time-intensive and resource-demanding, largely because of the low-throughput screening of full-length antibodies (approximately 1 × 10 variants) expressed in mammalian cells, which typically results in fe...
Predicting antibody affinity changes upon mutations by combining multiple predictors.
Scientific reports
Nov 11, 2020
Antibodies are proteins working in our immune system with high affinity and specificity for target antigens, making them excellent tools for both biotherapeutic and bioengineering applications. The prediction of antibody affinity changes upon mutatio...
Effective binding to protein antigens by antibodies from antibody libraries designed with enhanced protein recognition propensities.
mAbs
Jan 9, 2019
Antibodies provide immune protection by recognizing antigens of diverse chemical properties, but elucidating the amino acid sequence-function relationships underlying the specificity and affinity of antibody-antigen interactions remains challenging. ...
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