AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Antibodies

Showing 61 to 70 of 77 articles

Clear Filters

Challenges in antibody structure prediction.

mAbs
Advances in structural biology and the exponential increase in the amount of high-quality experimental structural data available in the Protein Data Bank has motivated numerous studies to tackle the grand challenge of predicting protein structures. I...

Paragraph-antibody paratope prediction using graph neural networks with minimal feature vectors.

Bioinformatics (Oxford, England)
SUMMARY: The development of new vaccines and antibody therapeutics typically takes several years and requires over $1bn in investment. Accurate knowledge of the paratope (antibody binding site) can speed up and reduce the cost of this process by impr...

Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery.

Briefings in bioinformatics
Antibodies are versatile molecular binders with an established and growing role as therapeutics. Computational approaches to developing and designing these molecules are being increasingly used to complement traditional lab-based processes. Nowadays,...

Heavy chain sequence-based classifier for the specificity of human antibodies.

Briefings in bioinformatics
Antibodies specifically bind to antigens and are an essential part of the immune system. Hence, antibodies are powerful tools in research and diagnostics. High-throughput sequencing technologies have promoted comprehensive profiling of the immune rep...

DLAB: deep learning methods for structure-based virtual screening of antibodies.

Bioinformatics (Oxford, England)
MOTIVATION: Antibodies are one of the most important classes of pharmaceuticals, with over 80 approved molecules currently in use against a wide variety of diseases. The drug discovery process for antibody therapeutic candidates however is time- and ...

Predicting antibody binders and generating synthetic antibodies using deep learning.

mAbs
The antibody drug field has continually sought improvements to methods for candidate discovery and engineering. Historically, most such methods have been laboratory-based, but informatics methods have recently started to make an impact. Deep learning...

BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning.

mAbs
Despite recent advances in transgenic animal models and display technologies, humanization of mouse sequences remains one of the main routes for therapeutic antibody development. Traditionally, humanization is manual, laborious, and requires expert k...

Deep Learning in Therapeutic Antibody Development.

Methods in molecular biology (Clifton, N.J.)
Deep learning applied to antibody development is in its adolescence. Low data volumes and biological platform differences make it challenging to develop supervised models that can predict antibody behavior in actual commercial development steps. But ...

Geometric potentials from deep learning improve prediction of CDR H3 loop structures.

Bioinformatics (Oxford, England)
MOTIVATION: Antibody structure is largely conserved, except for a complementarity-determining region featuring six variable loops. Five of these loops adopt canonical folds which can typically be predicted with existing methods, while the remaining l...

Antibody complementarity determining region design using high-capacity machine learning.

Bioinformatics (Oxford, England)
MOTIVATION: The precise targeting of antibodies and other protein therapeutics is required for their proper function and the elimination of deleterious off-target effects. Often the molecular structure of a therapeutic target is unknown and randomize...