Ref G2
Computational Service
Confidence Reported
Scoped After Feasibility Review
Service Overview
A co-crystal structure contains far more design information than the pose alone. We analyse experimental complexes in detail: interaction geometry and quality, the conserved and displaceable water network, ligand strain, the sub-pockets left unoccupied and the vectors available for growth. Where electron density is ambiguous, we say so, because a modelled side chain presented with false confidence has sent more than one series in the wrong direction.
Key Features & Advantages
Water Networks Analysed Seriously
Conserved, bridging and displaceable waters are identified and assessed energetically, and displacing the right one is often the cheapest affinity gain available in a series.
Interaction Quality, Not Only Presence
Hydrogen bonds are evaluated on distance and angle rather than counted, so weak or geometrically strained contacts are not mistaken for strong ones.
Growth Vectors Mapped
We identify which substituent positions point into open sub-pockets and which point at the protein or into solvent, turning the structure into a concrete design brief.
Density Quality Checked
Ligand and site occupancy, B-factors and fit to density are reviewed, and parts of the model not supported by the data are identified explicitly.
Where This Service Is Used
1Extracting design direction from a newly obtained co-crystal structure
2Comparing several complexes across a series to explain the observed SAR
3Identifying water molecules worth displacing for an affinity gain
4Understanding the structural basis of selectivity between a target and a close paralogue
5Preparing a validated structural model for docking, MM/GBSA or FEP work
Methods and Techniques Applied
1Structure quality assessment: resolution, R factors, ligand density fit, occupancy and B-factor analysis
2Complete interaction mapping including hydrogen bonds, hydrophobic contacts, pi systems and halogen bonds, with geometry evaluated
3Water network analysis with classification into conserved, bridging and displaceable positions
4Ligand conformational strain analysis relative to low-energy conformers
5Sub-pocket and vector analysis identifying growth directions and unoccupied space
6Cross-structure comparison across the series or across paralogues to explain SAR and selectivity
What We Need From You
1Structure files, with structure factors or maps where available for density assessment
2Measured activity data for the ligands in the structures, which anchors the interpretation
3The design question: growing, selectivity, potency, or explaining an SAR discontinuity
4Related structures of the same target or close homologues for comparison
What You Receive
1Detailed interaction analysis per complex with two- and three-dimensional diagrams
2Water network map with displacement candidates ranked
3Ligand strain assessment and conformational analysis
4Vector and sub-pocket map translated into specific design suggestions
5Prepared structures and viewer sessions ready for downstream computational work
What We Compute and Report
1Complete interaction inventory per complex with distances, angles and geometric quality
2Water network map with each water classified as conserved, bridging or displaceable
3Ligand strain energy relative to its accessible low-energy conformers
4Sub-pocket volumes and growth vectors available from each substituent position
5Cross-structure comparison quantifying what changes between series members or paralogues
Questions We Are Asked Before Scoping
Can you work with our unpublished structures?
▸Yes, under confidentiality. Unpublished internal structures are frequently the most valuable input to a design programme, and they remain yours.
What resolution is required?
▸Below roughly 2.5 Å, ligand geometry and water positions are usually reliable. Above 3 Å, water analysis becomes unreliable and we limit the conclusions accordingly.
Can you analyse a predicted complex instead?
▸Yes, but the analysis then inherits the model uncertainty, and water network conclusions in particular become speculative. We label predicted-complex analyses clearly.
Interpretation and Limitations
Computational predictions prioritise hypotheses; they do not independently establish binding, biological activity, therapeutic efficacy, safety or clinical validity. Performance depends on input quality, target class, the structural evidence available and the applicability domain of the selected methods. Scores used for ranking are comparative devices and should not be read as measured quantities. All deliverables are for research use only and are not intended for diagnostic or therapeutic use.
Is This the Right Service for You?
Choose this when
A structure has just been solved and every actionable detail is needed from it
Several complexes across a series must be compared to explain the SAR
Selectivity against a paralogue needs a structural explanation
Consider another service when
No experimental structure exists yet
AI Protein Structure Prediction The next decision is which analogue to make
Free Energy Perturbation What Goes Wrong, and How We Handle It
These are the failure modes that produce confident-looking results which do not survive the next experiment. Each one is designed against rather than discovered afterwards.
A ligand modelled into density that does not support it
Occupancy, B-factors and fit to density are reviewed, and ambiguous placements are reported as ambiguous.
Water molecules over-interpreted at moderate resolution
Water analysis is limited by resolution; above roughly 3 Å we state that displacement conclusions are not supportable.
Hydrogen bonds counted rather than evaluated
Distance and angle geometry is assessed for every contact, so weak interactions are not mistaken for strong ones.
A Worked Example
Turning one structure into a design brief
The situation
A 1.9 Å co-crystal had been solved and the team wanted to know where to grow.
What the work involved
Interactions were mapped with geometry assessed, the water network was classified into conserved and displaceable, ligand strain was computed, and sub-pockets were measured along each substituent vector.
What came back
Two growth vectors into unoccupied hydrophobic space, one displaceable water with an estimated gain, and one existing substituent shown to be strained and worth simplifying.
Illustrative of the shape this work takes. Details are generalised, no client or target is identified, and scope and outcome vary with the system in question.
What Published Work Reports
The results below were published by independent groups and are cited to show what these computational approaches have achieved in the literature. They are not ICDMO project results, and outcomes vary substantially between targets, datasets and programmes. We do not promise comparable results for any particular project.
Structure prediction now exceeds classical docking on independent benchmarks
On a held-out protein–ligand benchmark, deep-learning complex prediction placed roughly three quarters of ligands within 2 Å of the crystallographic pose, outperforming established docking programs on the same set. For design programmes this changes what can be attempted before a co-crystal structure exists — while leaving binding-mode certainty, and therefore the most consequential chemistry decisions, dependent on experimental structures.
Abramson et al., Nature, 2024 ↗Free energy methods can resolve the differences that decide a synthesis
Published benchmarks on congeneric series report errors near 1 kcal/mol, sufficient to rank analogues that differ by around tenfold in potency. In an integrated design loop this is the calculation reserved for decisions where making the wrong compound costs more than the computation.
Protocol and benchmark studies, Scientific Reports, 2019 ↗Service Workflow
1
Scientific Consultation
We discuss the objective, the decision it supports and the data you hold. Nothing is quoted before we understand what the result has to answer.
2
Feasibility Review
We check that the data, target class and available structural evidence can support the proposed approach, and say plainly where they cannot.
3
Technical Proposal
Scope, methods, deliverables, timeline and price are confirmed in writing, together with the limitations we expect to report.
4
Computation and QC
The analysis runs on pinned tool versions in a containerised environment, with quality checkpoints and method comparison where that is informative.
5
Report and Follow-Up
You receive the ranked results, the documented methods, the limitations and a proposed validation plan, plus support in interpreting them.
Service Process
1
Online Consultation
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2
Solution Matching
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3
Service Contract
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4
Project Execution
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Data Delivery
Specifications & Turnaround
Note: ICDMO delivers computational (in silico) services. Results prioritise hypotheses for testing; they do not by themselves establish binding, biological activity, efficacy or safety. All deliverables are for research use only and are not intended for diagnostic or therapeutic use. Downstream experimental work is scoped separately and executed through qualified partners.
Get a Custom Quote
Our scientific team responds within 24 hours with a detailed technical proposal and pricing tailored to your project.
Contact Us Online ConsultationStandard Deliverables
✓Sequence-verified final product
✓Full QC documentation
✓Certificate of Analysis (CoA)
✓Electronic data files
✓Project completion report
Why ICDMO?
Computational CDMO Focus
Reproducible, Versioned Pipelines
Confidence & Limitations Reported
Experiment-Aware Recommendations
Response Within One Business Day