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 …
Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms
M AlQuraishi, PK Sorger - Nature methods, 2021 - nature.com
Deep learning using neural networks relies on a class of machine-learnable models
constructed using 'differentiable programs'. These programs can combine mathematical …
constructed using 'differentiable programs'. These programs can combine mathematical …
Hopfield networks is all you need
We introduce a modern Hopfield network with continuous states and a corresponding
update rule. The new Hopfield network can store exponentially (with the dimension of the …
update rule. The new Hopfield network can store exponentially (with the dimension of the …
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 …
Antibody structure prediction using interpretable deep learning
Therapeutic antibodies make up a rapidly growing segment of the biologics market.
However, rational design of antibodies is hindered by reliance on experimental methods for …
However, rational design of antibodies is hindered by reliance on experimental methods for …
Germline-encoded amino acid–binding motifs drive immunodominant public antibody responses
EL Shrock, RT Timms, T Kula, EL Mena, AP West Jr… - Science, 2023 - science.org
Despite the vast diversity of the antibody repertoire, infected individuals often mount
antibody responses to precisely the same epitopes within antigens. The immunological …
antibody responses to precisely the same epitopes within antigens. The immunological …
[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 …
In silico proof of principle of machine learning-based antibody design at unconstrained scale
Generative machine learning (ML) has been postulated to become a major driver in the
computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to …
computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to …
Machine learning for biologics: opportunities for protein engineering, developability, and formulation
Successful biologics must satisfy multiple properties including activity and particular
physicochemical features that are globally defined as developability. These multiple …
physicochemical features that are globally defined as developability. These multiple …
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 …