Ref A2
Computational Service
Confidence Reported
Scoped After Feasibility Review
Service Overview
Before a screening or design campaign commits to a pocket, it is worth knowing whether that pocket can bind a drug-like molecule and hold it. We map the surface of your target for orthosteric, allosteric and cryptic sites, characterise each one geometrically and chemically, and score its druggability for the modality you intend to pursue. Where the apo structure hides a site, mixed-solvent and short simulation approaches are used to expose pockets that open only in the presence of a ligand.
Key Features & Advantages
All Site Classes, Not Only The Obvious One
Orthosteric, allosteric, interface and cryptic pockets are evaluated together, which matters for targets where the canonical site is conserved across a family and selectivity must come from elsewhere.
Cryptic Pocket Detection
Mixed-solvent MD and pocket-opening analysis are used to find transient sites absent from the deposited structure, with an honest statement of how confident the detection is.
Hot-Spot Residue Mapping
Fragment-probe mapping identifies the residues that contribute most binding energy, giving medicinal chemistry concrete anchor points rather than a pocket volume.
Consensus Across Independent Methods
Pockets are scored by several algorithms with different assumptions; agreement and disagreement between them are reported instead of a single number.
Where This Service Is Used
1Deciding whether a newly prioritised target is worth a screening campaign at all
2Locating an allosteric site when the active site is too conserved for selective inhibition
3Finding a tractable pocket on a protein–protein interaction target previously called undruggable
4Defining the docking box and constraints before high-throughput virtual screening begins
5Explaining why an earlier screen failed — often the chosen site is shallow, polar or solvent-exposed
Methods and Techniques Applied
1Geometric and energetic pocket detection across the full structure using several independent algorithms
2Druggability scoring based on volume, enclosure, hydrophobicity, polarity balance and hydrogen-bond capacity
3Fragment and probe mapping to identify hot-spot residues and sub-pocket preferences
4Mixed-solvent molecular dynamics and pocket-opening analysis for cryptic site detection
5Conservation and selectivity analysis across the protein family and relevant off-targets
6Water network analysis to identify displaceable waters that can be exploited for affinity
What We Need From You
1Experimental structure (PDB/mmCIF) or a sequence from which we build a model first
2The modality you intend to use and whether selectivity against a specific paralogue is required
3Known ligands, fragment hits or mutational data, which greatly improve site interpretation
4Any functional constraints — sites that must remain untouched, or an activity you intend to preserve
What You Receive
1Ranked pocket list with coordinates, volume, druggability scores and method agreement
2Annotated structural figures and session files showing each site and its hot-spot residues
3Selectivity assessment comparing the site against family members and likely off-targets
4Docking-ready target preparation: protonation states, site definition and constraints for downstream screening
5A written recommendation on which site to pursue, and where a site is judged not tractable, the reasons
What We Compute and Report
1Pocket volume, depth, enclosure and solvent exposure for every detected site
2Druggability score per site, with agreement across independent detection methods reported
3Per-residue hot-spot energies identifying which residues carry the binding potential
4Conservation score per pocket residue across the family, used to estimate achievable selectivity
5Water positions classified by displaceability, with the energetic cost of displacement estimated
Questions We Are Asked Before Scoping
What if our target has no experimental structure?
▸We predict or model the structure first and carry the model confidence through to the pocket analysis, so a site found only in a low-confidence region is labelled as such.
Does a good druggability score guarantee hits?
▸No. It indicates the site has the physical characteristics associated with drug binding. Whether chemical matter exists for it is answered by screening, not by scoring.
Can you assess a protein–protein interface?
▸Yes. Interfaces are evaluated for hot-spot concentration and for sub-pockets capable of holding a small molecule, which is usually the decisive question for PPI programmes.
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 target is chosen and you need to know whether a drug-like molecule can bind it at all
The active site is conserved across a family and selectivity has to come from somewhere else
A previous screening campaign produced nothing and the site itself is under suspicion
Consider another service when
A tractable pocket is already established and you need compounds
High-Throughput Virtual Screening The site only appears during simulation and needs mechanistic validation
Allosteric Drug Design 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 pocket that exists in the crystal form but is an artefact of crystal packing
Sites are checked against other structures of the same protein and tested for persistence in simulation before being recommended.
Scoring a site highly on volume alone when it is shallow and solvent-exposed
Enclosure, hydrophobic balance and hot-spot concentration are weighted alongside volume, and disagreement between methods is reported.
Cryptic pockets reported with more confidence than the sampling supports
Opening frequency and occupancy are quantified, and a site seen in one trajectory is labelled as a hypothesis, not a finding.
A Worked Example
Finding a selectivity handle on a conserved phosphatase
The situation
The catalytic site was nearly identical across the family and every reported inhibitor was promiscuous.
What the work involved
The full surface was mapped with several independent detection methods, mixed-solvent simulation was run to expose transient pockets, and every candidate site was scored for conservation across the family.
What came back
Two non-catalytic sites with acceptable druggability, one of which differed at four positions from the nearest paralogue. That site became the target of the subsequent screen.
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.
Around three quarters of predicted ligand poses fall within 2 Å
Current deep-learning structure prediction, evaluated on an independent protein–ligand benchmark, placed roughly 76% of ligands within 2 Å of the experimental pose, and reported approximately 80% by coordinate error — a substantial improvement over classical docking baselines on the same set, achieved without supplying a binding site. The corollary matters as much as the headline: roughly a quarter of cases still fall outside that threshold, which is why we report confidence per interface rather than delivering a single model.
Abramson et al., Nature, 2024 ↗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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Service Contract
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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