CD ComputaBio delivers Antibody Developability Assessment that puts computational prediction first: aggregation propensity, self-interaction and viscosity risk, chemical liability mapping, and conformational rigidity profiling are computed from sequence and structure before any assay is ordered. A minimal, hypothesis-driven wet-lab package—DSF, SEC, or AC-SINS—is then used to confirm the highest-risk predictions, so resources are focused where modeling indicates real developability liability. This integrated approach accelerates lead selection, engineering, and formulation readiness for antibody therapeutics.
We combine sequence-based aggregation propensity with solvent-accessibility weighting and surface patch detection to rank aggregation-nucleating regions in CDRs and frameworks, distinguishing buried motifs from truly exposed liabilities.
Electrostatic and hydrophobic surface properties are computed to estimate self-association tendency, flagging antibodies at risk for high-concentration viscosity, opalescence, or reversible self-association.
Sequence motifs and local structural context are evaluated together to rank deamidation, isomerization, oxidation, and cleavage liabilities, with emphasis on CDR positions where modification can impact binding.
Molecular dynamics and rigidity analysis reveal flexible loops and regions with high conformational entropy that may contribute to aggregation or instability, guiding stabilization strategies.
We design a focused experimental package—typically DSF for thermal stability, SEC for aggregation, and AC-SINS or similar for self-interaction—to confirm the highest-risk computational predictions, maximizing information per experiment.
Computational and confirmatory data are synthesized into a transparent developability index that ranks candidates side-by-side, highlighting the dominant risk mechanism for each molecule.
Sequence-based algorithms are combined with solvent-accessibility and local dynamics weighting to distinguish aggregation-prone segments that are buried from those that are exposed and therefore developability-relevant.
Surface charge distribution, dipole moment, and hydrophobic patch geometry are computed to predict self-interaction and colloidal behavior relevant to high-concentration antibody formulations.
Finite-temperature simulation reveals flexible CDR loops and framework regions that may contribute to aggregation, chemical modification, or conformational instability, guiding stabilization and re-surfacing decisions.
Sequence motifs and local structural context are evaluated to rank deamidation, isomerization, oxidation, and cleavage liabilities, with a focus on CDR positions where modification may affect binding or stability.
We synthesize aggregation, self-interaction, chemical liability, and conformational metrics into a transparent composite index that supports side-by-side candidate ranking and risk-weighted decision-making.
Rather than a standard panel, we recommend a minimal set of confirmatory assays targeted to the highest predicted risks, ensuring that experimental resources are spent on the most informative measurements.
We clarify the decision context—lead triage, variant selection, high-concentration risk, or formulation prioritization—and define the antibody panel, reference molecule, and any existing experimental data.
Experimental or modeled antibody structures are prepared with corrected loops, protonation states at the relevant pH, and annotation of CDRs, frameworks, and Fc regions to focus analysis on developability-relevant surfaces.
We run aggregation propensity, self-interaction and colloidal risk, chemical liability mapping, and conformational dynamics analysis in parallel, producing a multi-dimensional risk profile for each antibody.
Candidates are ranked by composite developability index, and dominant risk mechanisms are identified to generate specific hypotheses: which surface patches to engineer, which chemical liabilities to mutate, which buffer conditions to prioritize.
Based on the highest predicted risks, we recommend a minimal set of confirmatory assays—for example DSF for thermal stability, SEC for aggregation, AC-SINS for self-interaction—and support experimental design and data interpretation.
We deliver a decision-ready report that combines computational predictions, confirmatory data, and actionable recommendations for engineering, formulation, and candidate advancement, written for both discovery and CMC audiences.
Choose the question that best matches your program stage; we will recommend the computational focus and confirmatory assays that answer it.
| Developability Question | Recommended Computational Focus | Key Readouts | Decision Supported |
|---|---|---|---|
| Which antibody leads should advance to expression and purification? | Composite developability index, aggregation, self-interaction, chemical liability | Ranked candidate list, dominant risk mechanism per candidate, developability score | Allocate expression and purification resources to the most developable leads |
| Will a humanization or affinity-maturation variant retain developability? | Variant surface property comparison, aggregation and self-interaction delta | Per-variant developability score, liability changes, recommended re-surfacing positions | Approve, reject, or re-engineer variants before expression |
| Why is my antibody viscous or opalescent at high concentration? | Self-interaction, electrostatic and hydrophobic patch analysis, colloidal risk | Self-interaction tendency, patch map, viscosity risk classification | Re-surface engineer, adjust buffer, or set a concentration limit |
| Where are the chemical liabilities in my CDRs? | Chemical liability mapping with CDR focus and structural context | Ranked deamidation, isomerization, oxidation sites with CDR annotation | Mutate liability sites, adjust formulation, or plan analytical monitoring |
| Which buffer and excipients should I prioritize for formulation screening? | pH and excipient sensitivity analysis integrated with self-interaction prediction | pH stability window, excipient effect estimates, formulation ranking | Select lead formulation candidates for screening studies |
| Do I need wet-lab assays, or is computation sufficient? | Risk-based assay design and computational confidence assessment | Assay recommendation with justification, predicted risk level, expected information gain | Decide which experiments are necessary and which predictions can be trusted |
Before committing to expression and purification of dozens of antibody leads, we score each molecule for aggregation, self-interaction, and chemical liability risk to short-list the most developable subset.
When humanization or affinity maturation generates multiple variants, we compare surface properties and aggregation propensity to identify variants that retain developability without compromising binding.
For high-concentration antibody formulations, we flag electrostatic and hydrophobic patches that drive self-interaction and viscosity, guiding re-surfacing or buffer optimization before compounding.
When chemical modification is observed in stability studies, we map liabilities back to specific CDR positions, confirm mechanisms, and recommend mutations or formulation adjustments.
For antibodies entering formulation development, we estimate pH and excipient effects on self-interaction and chemical stability to nominate lead buffer candidates for screening studies.
When experimental resources are limited, we design a minimal confirmatory package targeted to the highest predicted risks, ensuring that each assay provides maximum information for decision-making.
Developability failures are expensive, but so are unnecessary assays. By predicting aggregation, self-interaction, chemical liability, and conformational risk before committing to wet-lab work, we help you focus experimental resources on the few candidates and few measurements that actually matter. Computational predictions are cross-validated against confirmatory data, so you get both speed and confidence.
Goal: rank 40 antibody leads by developability before committing to expression and purification.
Goal: choose humanized variants that retain developability while maintaining binding affinity.
Goal: understand why a lead antibody shows high viscosity at 150 mg/mL and identify mitigation strategies.
No, but it can dramatically reduce the number of assays needed. We use computation to rank candidates and identify specific liabilities, then recommend a minimal confirmatory package targeted to the highest predicted risks. Where assay data exist, we use them to calibrate and validate predictions, giving you both speed and confidence.
We model aggregation propensity, self-interaction and colloidal risk, chemical degradation (deamidation, isomerization, oxidation, and cleavage), and conformational flexibility as distinct mechanisms. Each is computed with the method best suited to its physics, then synthesized into a composite developability view.
A structure substantially improves accuracy because surface exposure and local dynamics drive aggregation and self-interaction. When only sequences are available, we can generate homology or AlphaFold models first, then run developability profiling. Some dimensions, such as sequence motif-based chemical liability screening, can provide useful initial estimates from sequence alone.
Yes. We compute changes in aggregation propensity, self-interaction risk, and chemical liability for each variant, and compare surface properties to the wild-type or reference antibody. This supports humanization, affinity maturation, and re-surfacing decisions by flagging mutations that improve binding at the cost of developability.
We extend developability modeling with pH, ionic-strength, and excipient-context sensitivity analysis to nominate lead formulation candidates for screening. For comparability and filing support, we produce multi-mechanism developability fingerprints aligned with assay data, providing defensible evidence for process and variant comparability claims.
Share your antibody sequences or structures, candidate panel, variant list, or formulation context. Our team will computationally profile aggregation, self-interaction, chemical liability, and conformational risk, design a minimal confirmatory assay package where needed, and deliver a decision-ready developability assessment with clear next steps for engineering and formulation.
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