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Antibodies, Monoclonal

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Deep learning framework for peak detection at the intact level of therapeutic proteins.

Journal of separation science
While automated peak detection functionalities are available in commercially accessible software, achieving optimal true positive rates frequently necessitates visual inspection and manual adjustments. In the initial phase of this study, hetero-varia...

Explainable deep learning enhances robust and reliable real-time monitoring of a chromatographic protein A capture step.

Biotechnology journal
The application of model-based real-time monitoring in biopharmaceutical production is a major step toward quality-by-design and the fundament for model predictive control. Data-driven models have proven to be a viable option to model bioprocesses. I...

Prediction of HIV sensitivity to monoclonal antibodies using aminoacid sequences and deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Knowing the sensitivity of a viral strain versus a monoclonal antibody is of interest for HIV vaccine development and therapy. The HIV strains vary in their resistance to antibodies, and the accurate prediction of virus-antibody sensitivi...

In silico proof of principle of machine learning-based antibody design at unconstrained scale.

mAbs
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 confirm this hypothesis have been hindered by the infeasibility of testing ...

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies.

mAbs
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-intensive endeavor. In this regard, progress in the generation of data describi...

Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics.

mAbs
Machine learning has been recently used to predict therapeutic antibody aggregation rates and viscosity at high concentrations (150 mg/ml). These works focused on commercially available antibodies, which may have been optimized for stability. In this...

Humanization of antibodies using a machine learning approach on large-scale repertoire data.

Bioinformatics (Oxford, England)
MOTIVATION: Monoclonal antibody (mAb) therapeutics are often produced from non-human sources (typically murine), and can therefore generate immunogenic responses in humans. Humanization procedures aim to produce antibody therapeutics that do not elic...

Calculation of therapeutic antibody viscosity with coarse-grained models, hydrodynamic calculations and machine learning-based parameters.

mAbs
High viscosity presents a challenge for manufacturing and drug delivery of therapeutic antibodies. The viscosity is determined by protein-protein interactions among many antibodies. Molecular simulation is a promising method to study protein-protein ...

Prediction of Contact Residues in Anti-HIV Neutralizing Antibody by Deep Learning.

Japanese journal of infectious diseases
The monoclonal antibody 1C10 targets the V3 loop of HIV-1 and neutralizes a broad range of clade B viruses. However, the mode of interaction between 1C10 and the V3 loop remains unclear because crystallization of 1C10 and the V3 peptide was unsuccess...