AIMC Topic: Antibodies

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Selectivity Approaches in Therapeutic Antibody Design.

Journal of medicinal chemistry
Protein therapeutics, particularly antibody-based therapies, have emerged as a cornerstone in modern disease treatment, offering key advantages over small molecules, including superior target specificity, longer half-life, and expanded target accessi...

ANABAG: Annotated Antibody-Antigen Data Set with Unique Features for Antibody Engineering Applications.

Journal of chemical information and modeling
The analysis and prediction of antibody-antigen (Ab-Ag) interactions often overlook critical structural features such as glycosylation and important physicochemical conditions like pH and salt concentration. Additionally, the field lacks standardized...

Artificial intelligence-driven computational methods for antibody design and optimization.

mAbs
Antibodies play a crucial role in our immune system. Their ability to bind to and neutralize pathogens opens opportunities to develop antibodies for therapeutic and diagnostic use. Computational methods capable of designing antibodies for a target an...

AbEpiTope-1.0: Improved antibody target prediction by use of AlphaFold and inverse folding.

Science advances
B cell epitope prediction tools are crucial for designing vaccines and disease diagnostics. However, predicting which antigens a specific antibody binds to and their exact binding sites (epitopes) remains challenging. Here, we present AbEpiTope-1.0, ...

Applying computational protein design to therapeutic antibody discovery - current state and perspectives.

Frontiers in immunology
Machine learning applications in protein sciences have ushered in a new era for designing molecules in silico. Antibodies, which currently form the largest group of biologics in clinical use, stand to benefit greatly from this shift. Despite the prol...

Development of an mPBPK machine learning framework for early target pharmacology assessment of biotherapeutics.

Scientific reports
Development of antibodies often begins with the assessment and optimization of their physicochemical properties, and their efficient engagement with the target of interest. Decisions at the early optimization stage are critical for the success of the...

Challenges and compromises: Predicting unbound antibody structures with deep learning.

Current opinion in structural biology
Therapeutic antibodies are manufactured, stored and administered in the free state; this makes understanding the unbound form key to designing and improving development pipelines. Prediction of unbound antibodies is challenging, specifically modellin...

The Application of Machine Learning on Antibody Discovery and Optimization.

Molecules (Basel, Switzerland)
Antibodies play critical roles in modern medicine, serving as diagnostics and therapeutics for various diseases due to their ability to specifically bind to target antigens. Traditional antibody discovery and optimization methods are time-consuming a...

ParaAntiProt provides paratope prediction using antibody and protein language models.

Scientific reports
Efficiently predicting the paratope holds immense potential for enhancing antibody design, treating cancers and other serious diseases, and advancing personalized medicine. Although traditional methods are highly accurate, they are often time-consumi...

Prediction of antibody-antigen interaction based on backbone aware with invariant point attention.

BMC bioinformatics
BACKGROUND: Antibodies play a crucial role in disease treatment, leveraging their ability to selectively interact with the specific antigen. However, screening antibody gene sequences for target antigens via biological experiments is extremely time-c...