Ref E2
Computational Service
Confidence Reported
Scoped After Feasibility Review
Service Overview
MM/GBSA and MM/PBSA occupy a useful middle ground: substantially more informative than docking scores, substantially cheaper than rigorous alchemical free energy. Calculated over molecular dynamics trajectories, they rank compounds within a congeneric series and, through per-residue decomposition, show which interactions carry the binding energy. We use them to triage tens of compounds down to the handful that merit FEP or synthesis, and we are explicit about where the approximation breaks down.
Key Features & Advantages
Ensemble-Based, Not Single-Structure
Energies are averaged over trajectory snapshots rather than computed from one minimised pose, which is the main reason the method outperforms docking scores for ranking.
Per-Residue Decomposition
Binding energy is broken down by residue, identifying the interactions that matter and the ones that are merely decorative — directly useful for analogue design.
Entropy Treated Transparently
Entropy contributions are either included with a stated method or deliberately omitted, and the choice is documented, because inconsistent treatment is a common source of misleading numbers.
Benchmarked Against Your Data
Where measured affinities exist for part of the series, we report the observed correlation so you know how far to trust the ranking for the rest.
Where This Service Is Used
1Ranking analogues in a lead optimisation series before committing synthesis effort
2Rescoring virtual screening hits to improve on docking-score ordering
3Identifying which interactions dominate binding for a given chemotype
4Comparing binding across wild-type and mutant forms of a target
5Selecting the small subset of compounds that justify the cost of FEP calculations
Methods and Techniques Applied
1Molecular dynamics production runs for each complex with consistent protocol across the series
2Snapshot extraction with a sampling interval chosen to limit correlation between frames
3MM/GBSA and MM/PBSA calculation with solvation model and parameter choices documented
4Per-residue energy decomposition and identification of the dominant contributions
5Optional entropy estimation by normal mode or interaction entropy approaches
6Correlation analysis against measured data where available, with the error range reported
What We Need From You
1Target structure and the binding poses for all compounds to be compared
2The compound series, ideally congeneric, since cross-series comparison is unreliable
3Measured affinity data for any subset, used to benchmark the ranking
4The decision the ranking must support, which sets how many compounds need treating
What You Receive
1Binding energy table with component breakdown for every compound
2Per-residue decomposition identifying the interaction hot spots
3Correlation plot and statistics against measured data where such data exists
4Ranked compound list with an explicit statement of which differences are within noise
5Full protocol record: force field, solvation model, sampling and entropy treatment
What We Compute and Report
1Estimated binding energy per compound with its component breakdown and statistical spread
2Per-residue energy decomposition identifying which interactions carry the binding
3Rank order across the series with an explicit statement of which gaps are within noise
4Correlation statistics against your measured affinities where a calibration set exists
5Sampling and convergence metrics for every complex analysed
Questions We Are Asked Before Scoping
How accurate is MM/GBSA?
▸Typical reported correlations with measured affinity fall in a broad and system-dependent range. It is a ranking tool for related compounds, not a predictor of absolute binding free energy.
Can we compare different chemical series?
▸Not reliably. Systematic errors largely cancel within a congeneric series and do not cancel across scaffolds. For cross-series comparison, alchemical methods or experiment are required.
Should we use this or FEP?
▸MM/GBSA to triage many compounds cheaply, FEP for the final few where an error of one kcal per mole matters. Used together they are more efficient than either alone.
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
Tens of analogues in one series need ordering before synthesis
Virtual screening hits need rescoring beyond docking-score ordering
You need to know which specific interactions carry the binding energy
Consider another service when
A small number of decisions justify the most accurate method available
Free Energy Perturbation The binding mode itself is still uncertain
Molecular Docking 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.
Comparing compounds across different scaffolds
Systematic errors cancel within a congeneric series and do not cancel across them; cross-series comparisons are declined or heavily caveated.
Entropy handled inconsistently between compounds
The entropy treatment is fixed for the whole series and stated in the report, rather than applied where it flatters the result.
Calculated energies read as predicted affinities
Output is presented as a ranking with its noise floor, and differences within that floor are called indistinguishable.
A Worked Example
Ordering 28 analogues for a synthesis round
The situation
A chemistry team could make eight compounds in the next cycle and had twenty-eight candidates on paper.
What the work involved
All complexes were simulated under one protocol, end-point energies were computed with per-residue decomposition, and the calculation was benchmarked against nine compounds already measured.
What came back
Correlation against the measured set was reported up front, six candidates were separated clearly from the rest, and two contributing interactions were identified that the team had not been optimising.
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.
Alchemical free energy calculations reach roughly 1 kcal/mol error
Benchmark studies on congeneric series across multiple drug targets report root-mean-square errors around 1 kcal/mol, with published protocol work reporting prediction errors below approximately 1.3 kcal/mol on test sets of ligand transformations. An error of this size is small enough to separate compounds differing by roughly one order of magnitude in affinity, which is what makes the method useful for deciding which analogue to synthesise.
Protocol and benchmark studies, Scientific Reports, 2019 ↗Accuracy is system-dependent, and retrospective benchmarks flatter it
Reviews of free energy methods in drug discovery note that much of the published evidence is retrospective, and that prospective performance in live optimisation campaigns is less thoroughly documented. This is why we validate against your own measured compounds before issuing prospective predictions, rather than quoting a literature error bar as though it applied to your system.
Review, Free Energy Calculations for Drug Discovery, 2021 ↗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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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