STRUCTURE ANALYSIS
& Model Validation

Expert assessment of experimental structures, AI-generated models and structural data in scientific context.

Make structural information reliable, interpretable and useful.

Structural models are most valuable when their quality, limitations and biological meaning are understood. This applies to experimental structures, AI-generated models, protein-ligand complexes, molecular assemblies and structures used for downstream analysis or communication.

At BIMOVIS, we analyse and validate structural data in the context of the scientific question. We assess what a structure supports, where interpretation needs caution and which features are relevant for the next project decision.

Structure analysis and model validation can support many stages of a project: from checking an AI-generated model before using it for visualization or simulation, to interpreting an experimental structure for drug discovery, protein engineering or mechanism-of-action studies.

The goal is not only to inspect a structure technically. The goal is to connect structural quality, molecular interpretation and project relevance.

Model quality, structural meaning and limits of interpretation.

Structure analysis helps identify the features that matter in a molecular model: binding sites, interfaces, conformational states, domain organization, residue interactions, surface properties or structural differences between variants.

Model validation adds the quality layer. It assesses whether a structure or predicted model is reliable enough for the intended use, whether certain regions require caution and whether further experimental or computational work would strengthen the interpretation.

At BIMOVIS, we combine structural biology expertise with computational analysis to provide a clear view of what a structure can tell you, and what it cannot. This is especially important when structures are used to guide decisions in drug discovery, protein engineering, AI model validation or scientific communication.

Quality assessment of experimental structures and AI-generated models
Interpretation of binding sites, interfaces and structural features
Identification of uncertain, flexible or poorly supported regions
Comparison of variants, homologs, conformational states or model versions
Assessment of model suitability for visualization, simulation, docking or design
Recommendations for validation, refinement or complementary structural biology work

How we work.

Question & structure review

We define the scientific question and review the available structures, models, maps, sequence information and project context.

Quality and confidence assessment

We assess model quality, local confidence, geometry, uncertainty, completeness and regions that may limit interpretation.

Structural analysis

We analyse project-relevant features such as binding sites, interfaces, domain organization, conformational changes or residue-level interactions.

Comparison and context

Structures are compared with homologs, variants, public data, experimental information or alternative models where this supports interpretation.

Project-focused interpretation

We translate the structural findings into conclusions relevant to the scientific question, application or next decision.

Recommendation for next steps

We define whether the structure is suitable for downstream use, needs refinement, requires validation or should be complemented by further experimental or computational work.

Question & structure review

We define the scientific question and review the available structures, models, maps, sequence information and project context.

Quality and confidence assessment

We assess model quality, local confidence, geometry, uncertainty, completeness and regions that may limit interpretation.

Structural analysis

We analyse project-relevant features such as binding sites, interfaces, domain organization, conformational changes or residue-level interactions.

Comparison and context

Structures are compared with homologs, variants, public data, experimental information or alternative models where this supports interpretation.

Project-focused interpretation

We translate the structural findings into conclusions relevant to the scientific question, application or next decision.

Recommendation for next steps

Structure analysis and model validation support projects where structural information needs to be interpreted carefully before it guides scientific, technical or communication decisions.

Where this service applies.

Explore how this service is used to address real structural biology challenges across drug discovery, protein engineering, and beyond.

AI Structure Model Validation

Assessment of predicted structures, confidence, uncertainty and suitability for downstream use.

Protein Engineering

Comparison of variants, structural stability, interfaces and design-relevant molecular features.

Antibody Epitope Mapping

Structural interpretation of antigen surfaces, interfaces and antibody-antigen recognition hypotheses.

PROTAC Design

Analysis of target structures, binding regions, complex hypotheses and structural information relevant to degrader design.

Structure-Based Drug Design

Interpretation of binding sites, ligand interactions and structural features relevant to CADD and structure-guided discovery.

Structure analysis questions.

What types of structures can be assessed through this service?

BIMOVIS can assess experimental structures, AI-generated models, protein-ligand complexes, molecular assemblies, structural variants, homologs, conformational states, and different model versions. The approach is adapted to the type of structural data available and the intended scientific use.

How does model validation differ from structural analysis?

Structure analysis focuses on the biological and molecular meaning of a structure, including binding sites, interfaces, domain organization, residue interactions, and conformational features. Model validation evaluates the quality, confidence, geometry, completeness, and uncertainty of the model to determine how reliably it can support the intended interpretation or application.

When should an AI-generated structure be validated before use?

Validation is particularly important when an AI-generated structure will be used for docking, molecular dynamics, protein engineering, drug discovery, mechanistic interpretation, or other decisions that depend on structural accuracy. The level of assessment should match the intended use and help identify regions that are uncertain, flexible, incomplete, or otherwise unsuitable for interpretation.

What kind of project decisions can structure analysis support?

Structure analysis can help determine whether a model is suitable for downstream simulation or design, whether a binding site or interface is structurally meaningful, whether variants may alter molecular interactions, whether further refinement is needed, or whether additional experimental or computational work would strengthen the conclusions.

How does BIMOVIS determine whether a structure is suitable for downstream use?

BIMOVIS evaluates structural quality, confidence, completeness, uncertainty, and project-relevant features in the context of the intended application. A model may be suitable for visualization but require additional assessment before being used for docking, simulation, structure-based design, or detailed mechanistic interpretation.

Extend your project.

AI Structure Prediction & Model Analysis

AI-supported model generation and analysis as a starting point for structural interpretation and validation.

Molecular Dynamics Simulations

Simulation-based analysis of flexibility, stability and interaction behavior beyond a static model.

Molecular Visualization

Scientific images, animations and visual formats that communicate structural findings clearly and accurately.

Go deeper in our Knowledge Hub.

Guides, protocols, and reference materials to support your structural biology projects — free to access.

STRUCTURE ANALYSIS & Model Validation