[HTML][HTML] 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 …
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 …
CoV-AbDab: the coronavirus antibody database
MIJ Raybould, A Kovaltsuk, C Marks… - Bioinformatics, 2021 - academic.oup.com
Motivation The emergence of a novel strain of betacoronavirus, SARS-CoV-2, has led to a
pandemic that has been associated with over 700 000 deaths as of August 5, 2020 …
pandemic that has been associated with over 700 000 deaths as of August 5, 2020 …
ABlooper: fast accurate antibody CDR loop structure prediction with accuracy estimation
Motivation Antibodies are a key component of the immune system and have been
extensively used as biotherapeutics. Accurate knowledge of their structure is central to …
extensively used as biotherapeutics. Accurate knowledge of their structure is central to …
Five computational developability guidelines for therapeutic antibody profiling
MIJ Raybould, C Marks, K Krawczyk… - Proceedings of the …, 2019 - National Acad Sciences
Therapeutic mAbs must not only bind to their target but must also be free from
“developability issues” such as poor stability or high levels of aggregation. While small …
“developability issues” such as poor stability or high levels of aggregation. While small …
[HTML][HTML] Advances in computational structure-based antibody design
Antibodies are currently the most important class of biotherapeutics and are used to treat
numerous diseases. Recent advances in computational methods are ushering in a new era …
numerous diseases. Recent advances in computational methods are ushering in a new era …
[HTML][HTML] ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins
Immune receptor proteins play a key role in the immune system and have shown great
promise as biotherapeutics. The structure of these proteins is critical for understanding their …
promise as biotherapeutics. The structure of these proteins is critical for understanding their …
Computational optimization of antibody humanness and stability by systematic energy-based ranking
A Tennenhouse, L Khmelnitsky, R Khalaila… - Nature biomedical …, 2024 - nature.com
Conventional methods for humanizing animal-derived antibodies involve grafting their
complementarity-determining regions onto homologous human framework regions …
complementarity-determining regions onto homologous human framework regions …
Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery
W Wilman, S Wróbel, W Bielska… - Briefings in …, 2022 - academic.oup.com
Antibodies are versatile molecular binders with an established and growing role as
therapeutics. Computational approaches to developing and designing these molecules are …
therapeutics. Computational approaches to developing and designing these molecules are …
Computational approaches to therapeutic antibody design: established methods and emerging trends
Antibodies are proteins that recognize the molecular surfaces of potentially noxious
molecules to mount an adaptive immune response or, in the case of autoimmune diseases …
molecules to mount an adaptive immune response or, in the case of autoimmune diseases …