mAbs

Papers
(The H4-Index of mAbs is 25. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-04-01 to 2024-04-01.)
ArticleCitations
Antibodies to watch in 2022235
Antibodies to watch in 2021234
Antibodies to watch in 2023110
Targeting cancer with antibody-drug conjugates: Promises and challenges85
BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning56
Simlukafusp alfa (FAP-IL2v) immunocytokine is a versatile combination partner for cancer immunotherapy52
Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies51
501Y.V2 and 501Y.V3 variants of SARS-CoV-2 lose binding to bamlanivimab in vitro48
Developments and opportunities in continuous biopharmaceutical manufacturing40
In silico proof of principle of machine learning-based antibody design at unconstrained scale37
Fc galactosylation follows consecutive reaction kinetics and enhances immunoglobulin G hexamerization for complement activation36
Ten years in the making: application of CrossMab technology for the development of therapeutic bispecific antibodies and antibody fusion proteins35
Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics35
Polyreactivity and polyspecificity in therapeutic antibody development: risk factors for failure in preclinical and clinical development campaigns33
A computational method for immune repertoire mining that identifies novel binders from different clonotypes, demonstrated by identifying anti-pertussis toxoid antibodies32
Coming together at the hinges: Therapeutic prospects of IgG332
Discovery-stage identification of drug-like antibodies using emerging experimental and computational methods32
Preclinical evaluation of AFM24, a novel CD16A-specific innate immune cell engager targeting EGFR-positive tumors31
One size does not fit all: navigating the multi-dimensional space to optimize T-cell engaging protein therapeutics30
Host cell protein profiling of commercial therapeutic protein drugs as a benchmark for monoclonal antibody-based therapeutic protein development29
Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics29
Calculation of therapeutic antibody viscosity with coarse-grained models, hydrodynamic calculations and machine learning-based parameters27
Cadonilimab, a tetravalent PD-1/CTLA-4 bispecific antibody with trans-binding and enhanced target binding avidity26
Identifying biophysical assays and in silico properties that enrich for slow clearance in clinical-stage therapeutic antibodies26
Drug-like antibodies with high affinity, diversity and developability directly from next-generation antibody libraries25
Ab-Ligity: identifying sequence-dissimilar antibodies that bind to the same epitope25
Assay format diversity in pre-clinical immunogenicity risk assessment: Toward a possible harmonization of antigenicity assays25
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