What target systems can you handle?

We work with soluble proteins (enzymes, receptors) and membrane proteins where a structural model is available. Project feasibility is assessed case by case; please provide your target details during consultation.

How do you validate the predictions?

Whenever experimental binding data (IC50, Ki) are available, we calibrate scoring parameters and report Pearson or Spearman correlation. For novel targets, we use decoy sets and cross-validation to estimate ranking reliability.

Do you provide experimental affinity measurement as part of this service?

This service is computational only. If experimental validation is desired, we can recommend parallel services or coordinate with your preferred CRO for SPR/BLI measurements under separate scope.

How large can the ligand library be?

We routinely handle libraries from tens to thousands of compounds. Larger libraries can be processed with project-specific scheduling and compute resource allocation; please discuss during scoping.

What if I don't have experimental data for calibration?

We can still deliver rank-ordered predictions using default parameters and provide confidence metrics based on benchmark tests. However, calibration with even a small set of experimental affinities significantly improves accuracy.

Can you incorporate molecular dynamics refinement?

Yes, MD simulations (explicit solvent, 5–20 ns) can be added for a subset of top compounds to account for protein flexibility and solvent effects. The depth and cost are scoped per project.

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Computational Binding Affinity Analysis

Binding Affinity Prediction

Rank synthetic candidates by predicted binding free energy before in vitro testing, reducing experimental burden and accelerating lead optimization.

Target–ligand docking with multiple search algorithms
Physics-based and empirical scoring (MM/GBSA, LIE, deep learning)
Optional MD refinement for ensemble-averaged free energies
Validation against available experimental affinities (IC50, Ki)
Project-scoped integration with downstream screening data

Request Analysis

How accurate are the predictions?

Our computational pipeline is calibrated and validated using experimental binding data for each project, ensuring project-specific confidence levels.

Pose selection via consensus scoring across multiple docking engines
Free energy calculations with LIE, MM/GBSA, or deep learning models
Conformational sampling via short MD simulations where appropriate
Rank-ordering metrics correlated with measured affinity data

Validated

Customizable

Scalable

Our Methodology

Integrated computational workflow for binding affinity estimation

Combines molecular docking, free energy calculations, and experimental data calibration to deliver project-specific compound rankings.

Core Pipeline: Docking & Free Energy

Uses molecular docking to generate candidate ligand poses, then applies physics-based or empirical scoring functions (LIE, MM/GBSA) to compute binding free energies. Calibrated with available experimental data for each target system.

Target structure preparation from PDB, homology models, or cryo-EM
Ligand library preparation (SMILES, 3D conformer generation)
Multi-engine docking (AutoDock, Glide, or GOLD) and pose selection
Binding free energy calculation via LIE with optimized coefficients or MM/GBSA

Experimental Data Integration

Maximizes the value of available experimental data by using measured affinities to calibrate scoring parameters and validate predictions, following protocols that leverage sparse experimental data.

Optimization of computational coefficients against IC50/Ki data
Cross-validation with external test sets to estimate prediction reliability
Iterative refinement: docking → scoring → MD refinement → re-scoring
Correlation analysis between predicted and experimental affinities

Quality Control

Each ligand pose is visually inspected and subjected to steric clash checks. Docking scores are benchmarked against known actives and decoys to ensure enrichment.

Statistical Rigor

Rankings are accompanied by confidence metrics such as Pearson correlation with training data, and bootstrapped uncertainty estimates for each predicted ΔG.

Biological Interpretability

Key interaction fingerprints (hydrogen bonds, hydrophobic contacts, π-stacking) are extracted to explain predicted affinity differences and guide medicinal chemistry.

What You Receive

Comprehensive binding affinity analysis deliverables

Raw data, curated results, and interpretable reports designed for direct use in lead optimization.

Data Deliverables

Docked poses for each ligand (PDB or SDF format)
Docking scores and consensus rankings
Binding free energy estimates (LIE, MM/GBSA, or ML-based)
Correlation plots with experimental affinities (when available)
Interaction fingerprint summary per compound

Reporting & Support

Detailed project report with methods, settings, and results
Rank-ordered candidate lists with confidence indicators
Optional MD trajectory files and analysis scripts
Consultation session to discuss results and next steps

Project Scope & Customization

Case-by-case configuration for your target system

We tailor the analysis to your target, ligand library size, available experimental data, and desired computational depth.

Parameter	Typical project scope
Target protein preparation	PDB structure, homology model, or AlphaFold model; optional loop refinement, protonation state assignment
Ligand library size	Up to thousands of compounds; conformer generation and tautomer enumeration as needed
Docking engine	AutoDock Vina, Glide, GOLD, or consensus approach; customizable search space
Scoring method	LIE with system-specific coefficient, MM/GBSA, or deep learning (Boltz-2, Atomic Environment Vectors)
Molecular dynamics	Optional explicit solvent MD for ensemble refinement (5–20 ns per complex, scoped per project)
Validation strategy	Cross-validation against available experimental data; external test set or decoy set
Reporting format	PDF report, raw data files (CSV, PDB, DCD), and interactive plots upon request

How It Works

Streamlined computational binding affinity estimation workflow

Our stepwise process ensures reproducibility, traceability, and clear communication at each stage.

1. Target & Library Preparation

Protein structure is prepared from PDB, homology model, or cryo-EM with proper protonation and missing loop modeling. Ligand libraries are generated (SMILES → 3D conformers) and tautomeric states enumerated.

2. Molecular Docking

Each ligand is docked into the binding site using multiple search algorithms. The top-ranked poses are selected based on docking score consensus, and interaction fingerprints are extracted.

3. Binding Free Energy Calculation

Selected poses are submitted to end-point methods (LIE, MM/GBSA) or deep learning models to compute binding free energies. If project scope includes MD, short explicit-solvent simulations refine the complex.

4. Validation & Calibration

Predicted affinities are correlated with available experimental IC50/Ki data to calibrate scoring parameters. Cross-validation on held-out compounds provides confidence estimates.

5. Reporting & Delivery

Results are compiled into a structured report including rank-ordered lists, predicted ΔG values, interaction fingerprints, and optional MD analysis. A consultation meeting is provided to discuss findings.

Applications

Where computational binding affinity analysis adds value

From lead optimization to early candidate ranking, our service supports key drug discovery decisions.

Rank synthetic analogues to prioritize synthesis

Predict relative binding free energies within a congeneric series to guide medicinal chemistry cycles and reduce wet-lab screening burden.

Prioritize analogues for synthesis based on predicted affinity
Identify key interactions driving binding selectivity
Support scaffold hopping and isosteric replacement decisions

Refine high-throughput docking hits with accurate free energy

After initial virtual screening, apply free energy calculations to top-ranked hits to improve hit selection before experimental confirmation.

Re-rank top 100–1000 hits from docking with more accurate methods
Enrich hit rates in secondary assays
Filter false positives due to crude scoring

Predict impact of point mutations on drug binding

Assess how mutations in the target protein alter binding affinity of lead compounds, helping to anticipate resistance emergence.

Model mutant structures (e.g., single-point mutations) and re-dock
Compute binding free energy differences (ΔΔG) for wild-type vs mutant
Guide design of inhibitors with broader mutation coverage

Frequently Asked Questions

Common questions about computational binding affinity analysis

We address typical concerns about accuracy, scope, and how the service fits into your workflow.

We work with soluble proteins (enzymes, receptors) and membrane proteins where a structural model is available. Project feasibility is assessed case by case; please provide your target details during consultation.
Whenever experimental binding data (IC50, Ki) are available, we calibrate scoring parameters and report Pearson or Spearman correlation. For novel targets, we use decoy sets and cross-validation to estimate ranking reliability.
This service is computational only. If experimental validation is desired, we can recommend parallel services or coordinate with your preferred CRO for SPR/BLI measurements under separate scope.
We routinely handle libraries from tens to thousands of compounds. Larger libraries can be processed with project-specific scheduling and compute resource allocation; please discuss during scoping.
We can still deliver rank-ordered predictions using default parameters and provide confidence metrics based on benchmark tests. However, calibration with even a small set of experimental affinities significantly improves accuracy.
Yes, MD simulations (explicit solvent, 5–20 ns) can be added for a subset of top compounds to account for protein flexibility and solvent effects. The depth and cost are scoped per project.

Scientific Literature

References

Zasada SJ, Wright DW, Coveney PV. Large-scale binding affinity calculations on commodity compute clouds. Interface focus. 2020;10(6):20190133. View on PubMed
Passaro S, Corso G, Wohlwend J, et al. Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction. bioRxiv: the preprint server for biology. 2025 Jun 18. View on PubMed
Abdel-Rehim A, Orhobor O, Hang L, et al. Protein-ligand binding affinity prediction exploiting sequence constituent homology. Bioinformatics (Oxford, England). 2023;39(8). View on PubMed
Gu Y, Zhang X, Xu A, et al. Protein-ligand binding affinity prediction with edge awareness and supervised attention. iScience. 2023;26(1):105892. View on PubMed
Meli R, Anighoro A, Bodkin MJ, et al. Learning protein-ligand binding affinity with atomic environment vectors. Journal of cheminformatics. 2021;13(1):59. View on PubMed

Please kindly note that our products and services are for research use only.