Free Energy Perturbation (FEP / RBFE)

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Overview

Alchemical free energy calculation is the most accurate binding prediction routinely available, with reported accuracy approaching one kilocalorie per mole for well-behaved systems. It is also expensive, so it belongs at the point in lead optimisation where a wrong synthesis decision costs more than the calculation. We run relative and, where appropriate, absolute binding free energy calculations with careful perturbation mapping, convergence checking and validation against your existing measured data.

Schematic: replicate simulations are run and plotted together. A conclusion drawn from one trajectory cannot be distinguished from a sampling accident.

Key Advantages

Validated Against Your Own Series

Before predicting untested compounds, we reproduce measured affinities for compounds you have already made, so the expected error on your system is known rather than quoted from a paper.

Perturbation Maps Designed Carefully

Network topology, cycle closure and intermediate states are planned to give redundancy, which both improves accuracy and exposes individual calculations that have failed.

Convergence Checked Per Edge

Every transformation is assessed for phase-space overlap, hysteresis and convergence; edges that fail are rerun or reported as unreliable rather than averaged into the result.

Scoped To Decisions

We advise where FEP is worth its cost and where MM/GBSA suffices, because applying it indiscriminately wastes budget without improving decisions.

Where This Service Is Used

Choosing which analogues to synthesise in a lead optimisation cycle

Predicting the potency effect of a specific substituent change before making it

Assessing selectivity by calculating binding to the target and to a close off-target

Quantifying the effect of a resistance mutation on binding affinity

Prioritising a final candidate shortlist where potency differences are small but consequential

Methods and Techniques Applied

System preparation with consistent protonation, tautomer and binding mode assignment across the series

Perturbation network design with cycle closure redundancy and controlled transformation size

Lambda window and soft-core scheduling, with enhanced sampling for slow degrees of freedom where required

Relative binding free energy calculation, and absolute calculations where the series demands it

Convergence diagnostics: overlap, hysteresis, cycle closure error and replicate agreement

Retrospective validation against measured affinities before prospective predictions are issued

What We Need From You

High-quality target structure, preferably a co-crystal with a reference ligand of the series

The congeneric series with consistent, confirmed binding mode across members

Measured affinities for a validation subset, which is effectively a prerequisite for a credible study

The specific transformations you intend, or the design question that should drive the network

What You Receive

Predicted relative binding free energies with statistical uncertainty per transformation

Perturbation network diagram with cycle closure errors shown

Retrospective validation results establishing the expected error on your system

Ranked compound recommendations with an explicit statement of which differences are significant

Full protocol and input record enabling the calculation to be repeated or extended

What We Compute and Report

Relative binding free energy per transformation, in kcal/mol, with statistical uncertainty

Cycle closure errors across the perturbation network, exposing individually unreliable edges

Phase-space overlap and hysteresis diagnostics per lambda window

Retrospective accuracy on your measured compounds, establishing the expected error for your system

Ranked prospective predictions with the differences that are and are not statistically meaningful

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.

Specifications

Item	Specification	Notes
Transformations per study	10–100 edges	Network size set by the design question
Typical turnaround	3–6 weeks	Including validation and convergence checking
Reported accuracy	≈1 kcal/mol	For well-behaved systems; system-dependent and stated per project
Prerequisite	Consistent binding mode	Series must share a confirmed pose

Service Workflow

Step 01

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.

Step 02

Feasibility Review

We check that the data, target class and available structural evidence can support the proposed approach, and say plainly where they cannot.

Step 03

Technical Proposal

Scope, methods, deliverables, timeline and price are confirmed in writing, together with the limitations we expect to report.

Step 04

Computation and QC

The analysis runs on pinned tool versions in a containerised environment, with quality checkpoints and method comparison where that is informative.

Step 05

Report and Follow-Up

You receive the ranked results, the documented methods, the limitations and a proposed validation plan, plus support in interpreting them.

Is This the Right Service for You?

Choose this service when

A synthesis decision is expensive enough to justify the most accurate available method

A congeneric series shares a confirmed binding mode and some measured affinities exist

Selectivity between a target and a close off-target needs quantifying

Consider another service when

Many compounds need cheap triage before the expensive calculation

MM/GBSA & MM/PBSA Binding Free Energy

The scaffold changes substantially between compounds

De Novo Small Molecule Design

What Goes Wrong, and How We Handle It

The failure modes that produce confident-looking results which do not survive the next experiment. Each one is designed against rather than discovered afterwards.

Binding mode consistency is checked before the project is accepted, since the calculation assumes it.

Case Studies

The situation

Two vectors were available for optimisation, each requiring a different and costly synthetic route.

What the work involved

A perturbation network was designed with cycle redundancy across both vectors, validated retrospectively against eleven measured compounds, then run prospectively on the untested designs.

What came back

Retrospective error on the series was established and reported. One vector was predicted to gain roughly an order of magnitude, the other nothing measurable; the team committed to a single route.

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.

Questions We Are Asked Before Scoping

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 in Touch
about Free Energy Perturbation (FEP / RBFE)

Tell us about the target, the data you hold and the decision the result needs to support. A scientist reviews every enquiry and replies within one business day.

Feasibility reviewed before any scope or price is fixed

Methods, parameters, confidence and limitations in every report

Confidentiality agreement available before data is shared