AIXC BioSciences Platform Molecular Docking
Molecular Docking

Multiple46 Engines.
Consensus Scoring.
One Pipeline.

Run your target against multiplefoursix complementary docking engines in parallel. Consensus scoring eliminates false positives. Batch processing handles 400+ structures per target.

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MULTI-ENGINE DOCKINGFOUR DOCKING ENGINESSIX DOCKING ENGINES

Every Engine Has a Specialty

From fast virtual screening to physics-based refinement to generative deep learning approaches. Each engine contributes a unique perspective to the consensus.

Multiple Specialized Engines
Comprehensive Binding Analysis
Multiple specialized docking engines for comprehensive binding analysis. From fast virtual screening to physics-based refinement to generative deep learning approaches.
AutoDock Vina
Fast Virtual Screening
GPU optimized. Exhaustive search with Iterated Local Search global optimizer. Ideal for large-scale virtual screening campaigns.
HADDOCK3
Physics-Based Refinement
Information-driven docking with restraint support. Explicit solvent refinement. Gold standard for protein-protein and protein-peptide interactions.
DiffDock
Diffusion-Based Generative
State-of-the-art deep learning diffusion model. Generates diverse binding poses without predefined binding sites. Excellent for blind docking scenarios.
FlexPepDock
Rosetta Peptide Docking
Full backbone and side-chain flexibility for peptide-protein complexes. Rosetta energy function optimization. Ideal for linear peptide therapeutics.
ADCP / CrankPep
Cyclic & Flexible Peptides
Specialized for cyclic and conformationally flexible peptides. AutoDock CrankPep engine handles ring closures and disulfide constraints natively.
Chai-1
Multi-Backend AI
Six solver backends for structure prediction and docking. Combines deep learning with physics-based scoring. Handles proteins, nucleic acids, and small molecules.
METHODOLOGY

Consensus Scoring Pipeline

Individual engines can produce false positives. Our consensus approach ranks poses by agreement across multiple independent scoring functions, dramatically improving hit rates.

How Consensus Works

Each engine scores binding poses independently using different physics and statistical potentials. Poses that rank highly across multiple engines have the highest confidence.

Rank Aggregation Z-Score Normalization RMSD Clustering Contact Analysis
1
Independent Docking
Each engine generates 50-100 poses independently with its native scoring function.
2
Score Normalization
Z-score normalization across engines to make scores comparable on a unified scale.
3
Pose Clustering
RMSD-based clustering identifies convergent binding modes across engines.
4
Rank Aggregation
Final ranking by multi-engine agreement. Top poses reported with confidence intervals.
4+46
Docking Engines
400+
Structures / Target
3x
Hit Rate vs Single Engine
<24h
Batch Turnaround
WORKFLOW

From Target to Ranked Hits

Submit your protein target and compound library. Our automated pipeline handles preparation, docking, scoring, and visualization.

1
Structure Prep
Protein preparation, protonation, grid generation. Ligand 3D conformers and energy minimization.
2
Multi-Engine Docking
All available46 engines run in parallel. 50-100 poses per engine per compound.
3
Consensus Scoring
Z-score normalization, RMSD clustering, rank aggregation across all engines.
4
Results & Report
Ranked hits with 3D visualizations, binding mode analysis, and interaction maps.
GET STARTED

Ready to Dock Your Targets?

Submit your protein structure and compound library. We will run all availablefoursix engines and deliver consensus-ranked results with full binding mode analysis.

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