- Predict antibody structure using a fully guided homology modeling workflow that incorporates de novo CDR loop conformation prediction
- Perform batch homology modeling to accelerate model construction for a parent sequence and its variants
- Identify and prioritize promising leads by modeling and triaging antibody sequences with prediction tools for structure characterization
Antibody Design
Capabilities for structure-based design of antibodies
In traditional IgG or alternative formats
Construct reliable 3D structural models of antibodies directly from sequence
Streamline rational antibody humanization
- Generate humanized antibodies through CDR grafting in conjunction with targeted residue mutations
- Evaluate the percentage of humanness of resulting constructs
Understand and predict antibody-antigen interactions
- Predict antibody-antigen complex structures through ensemble protein-protein docking
- Enhance resolution of experimental epitope mapping data (e.g. mutagenesis or mass-spectroscopy) from peptide to residue level detail
- Identify favorable antibody-antigen contacts through fast protein-protein docking
- Interrogate and analyze predicted protein-protein interactions with an easily accessible graphical user interface
Derisk development by uncovering potential liabilities earlier
- Identify and prioritize promising leads by modeling and triaging antibody sequences with prediction tools for structure characterization
- Highlight potential surface sites for post-translational modification and chemical reactivity
- Detect potential hotspots for aggregation using computational protein surface analysis
Engineer antibodies in silico
- Accurately predict the impact of residue substitution on binding affinity, selectivity, and thermostability
- Rapidly identify high quality protein variants using Residue Scan FEP+ with lambda dynamics
- Refine antibody candidate selection using Protein Mutation FEP+ with an accuracy that reproduces experimentally determined relative free energies to within ~1 kcal/mol
- Perform multi-parameter optimization with easily amenable computational workflows e.g. simultaneously optimize for stability, affinity, cross-reactivity, aggregation propensity, and post-translational modification risk
Design high-quality biologics with Schrödinger’s cutting-edge software
BioLuminate
Modeling environment for biologics discovery
LiveDesign
Collaborative digital biologics design and discovery lab
Learn antibody modeling with our hands-on online course
Level-up your skills by enrolling in our online course, Introduction to Computational Antibody Engineering.
Learn MoreWebinars
Discover how Schrödinger technology is being used to solve real-world research challenges.
Optimizing protein stability using new computational design approaches for biologics
Accelerating antibody drug discovery through computational modeling
Antibody humanization guided by computational modeling
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Desmond
High-performance molecular dynamics (MD) engine providing high scalability, throughput, and scientific accuracy
Publications
Browse the list of peer-reviewed publications using Schrödinger technology in related application areas.
Robust Prediction of Relative Binding Energies for Protein–Protein Complex Mutations Using Free Energy Perturbation Calculations
Sampson, J. M. et al. Journal of Molecular Biology. 2024
Structure-guided engineering of immunotherapies targeting TRBC1 and TRBC2 in T cell malignancies
Ferrari, M. et al. Nat Commun. 2024
Biological activity validation of a computationally designed Rituximab/CD3 T cell engager targeting CD20+ cancers with multiple mechanisms of action
Cai, W. et al. Antibody Therapeutics, 2021, 4(4), 228-241
Relative Binding Affinity Prediction of Charge-Changing Sequence Mutations with FEP in Protein–Protein Interfaces
Clark, A. J. et al. Journal of Molecular Biology, 2019, 431(7), 1481-1493
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