free-energy-calculations

Name: free-energy-calculations
Author: GPTomics

1.1kstars185forks

How to install free-energy-calculations?

gptomics/bioskills/free-energy-calculations

$npx -y skills add GPTomics/bioSkills --skill free-energy-calculations

Installs into the current project.

Summary

Performs alchemical free-energy calculations including relative binding free energy (RBFE / FEP+) and absolute binding free energy (ABFE) via OpenFE, FEP+, GROMACS, AMBER pmemd, and OpenMM with explicit lambda scheduling, soft-core potentials, MBAR/BAR analysis, cycle-closure val

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SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: OpenFE 1.7+, OpenMM 8.1+, GROMACS 2024+, AMBER pmemd 22+, alchemlyb 2.1+, pymbar 4.0+, RDKit 2024.09+.
4
5	Before using code patterns, verify installed versions match. If versions differ:
6	- Python: `pip show <package>` then `help(module.function)` to check signatures
7	- CLI: `openfe --version`; `gmx --version`; `pmemd.cuda --version`
8
9	If code throws ImportError, AttributeError, or TypeError, introspect the installed
10	package and adapt the example to match the actual API rather than retrying.
11
12	# Free Energy Calculations
13
14	Predict binding free-energy differences (RBFE) or standard binding free energies (ABFE) using alchemical methods. FEP+ is a commercial workflow and OpenFE is an open-source framework. Accuracy and cost vary substantially with system, perturbation, force field, setup, sampling, and evaluation design; report the protocol and benchmark relevant to the intended decision. The Boltz-2 report includes benchmark-specific comparisons with FEP methods but does not replace prospective validation on the project chemistry.
15
16	For docking input poses, see `chemoinformatics/virtual-screening`. For pose validation before FEP, see `chemoinformatics/pose-validation`. For ML alternatives, see `chemoinformatics/ml-docking-rescoring`.
17
18	## FEP Method Taxonomy
19
20	\| Method \| Cost / pair \| Accuracy \| Use case \| Fails when \|
21	\|--------\|-------------\|----------\|----------\|------------\|
22	\| FEP+ (Schrödinger) \| System- and protocol-dependent GPU cost \| Published commercial RBFE workflow \| Commercial lead optimization \| License and reproducibility constraints \|
23	\| OpenFE RBFE \| System- and protocol-dependent GPU cost \| Open-source RBFE with documented protocols \| Open-source campaigns \| Mapping/setup/sampling require review \|
24	\| OpenFE ABFE \| Generally more setup and sampling than one RBFE edge \| Standard binding free energy \| No congeneric reference ligand required \| Restraints and end-state corrections \|
25	\| GROMACS / AMBER RBFE \| Implementation-dependent \| Custom alchemical workflows \| Expert-controlled setup \| Manual validation burden \|
26	\| FEP-SPell-ABFE \| Protocol/system-dependent \| Automated ABFE workflow \| Evaluate published and project benchmarks \| Limited adoption \|
27	\| QligFEP v2.1 \| Protocol/system-dependent \| Q-based ligand FEP \| Evaluate published and project benchmarks \| Different approximations/tooling \|
28	\| MM/PBSA / MM/GBSA \| Lower-cost endpoint analysis \| Approximate endpoint score \| Exploratory within-series comparison \| Entropy, sampling, and model dependence \|
29	\| Boltz-2 affinity \| seconds GPU \| 0.66 Pearson on reported FEP benchmark subset \| ML alternative; reported >=1000x lower cost \| Novel chemotypes \|
30	\| ALEPB / EE-AMBER \| Protocol/system-dependent \| Specialized methods \| Evaluate matched evidence \| Limited tooling \|
31
32	Decision: For congeneric lead-optimization questions, evaluate a validated RBFE protocol and perturbation network. Use endpoint methods only for decisions supported by a project-specific benchmark. Candidate counts and escalation gates should follow compute budget, uncertainty, and prospective validation rather than a universal top-N rule.
33
34	## Decision Tree by Scenario
35
36	\| Scenario \| Recommended workflow \|
37	\|----------\|---------------------\|
38	\| Rank close analogs (R-group SAR) \| RBFE via OpenFE (cycle: lig1↔lig2↔lig3) \|
39	\| Cross-scaffold ranking \| ABFE per ligand; or coordinated RBFE with star network \|
40	\| Congeneric lead-optimization set \| RBFE with a connected, redundancy-aware perturbation graph \|
41	\| Single ligand affinity \| ABFE (no reference needed) \|
42	\| Lower-cost exploratory ranking \| A project-validated endpoint or ML method, followed by orthogonal confirmation \|
43	\| Novel scaffold prospective \| Treat ML affinity as triage; validate selected decisions prospectively \|
44	\| Selectivity (target vs off-target) \| RBFE on both proteins; report delta-delta-G \|
45	\| Allosteric vs orthosteric \| ABFE comparable; check pose stability with MD \|
46	\| Ions / metal centers \| Specialized force field (ZAFF, MCPB.py); not standard FEP \|
47
48	## Relative Binding Free Energy (RBFE) Setup
49
50	Goal: Calculate delta-delta-G between two ligands (lig1 -> lig2) in pocket.
51
52	Approach: Alchemical transformation lig1 -> lig2 in both bound state (pocket + ligand + water) and unbound state (ligand + water alone). Thermodynamic cycle:
53
54	```
55	delta(delta-G_binding) = (delt

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free-energy-calculations

1.1kstars185forks

View on GitHub

How to install free-energy-calculations?

gptomics/bioskills/free-energy-calculations

$npx -y skills add GPTomics/bioSkills --skill free-energy-calculations

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: OpenFE 1.7+, OpenMM 8.1+, GROMACS 2024+, AMBER pmemd 22+, alchemlyb 2.1+, pymbar 4.0+, RDKit 2024.09+.
4
5	Before using code patterns, verify installed versions match. If versions differ:
6	- Python: `pip show <package>` then `help(module.function)` to check signatures
7	- CLI: `openfe --version`; `gmx --version`; `pmemd.cuda --version`
8
9	If code throws ImportError, AttributeError, or TypeError, introspect the installed
10	package and adapt the example to match the actual API rather than retrying.
11
12	# Free Energy Calculations
13
14	Predict binding free-energy differences (RBFE) or standard binding free energies (ABFE) using alchemical methods. FEP+ is a commercial workflow and OpenFE is an open-source framework. Accuracy and cost vary substantially with system, perturbation, force field, setup, sampling, and evaluation design; report the protocol and benchmark relevant to the intended decision. The Boltz-2 report includes benchmark-specific comparisons with FEP methods but does not replace prospective validation on the project chemistry.
15
16	For docking input poses, see `chemoinformatics/virtual-screening`. For pose validation before FEP, see `chemoinformatics/pose-validation`. For ML alternatives, see `chemoinformatics/ml-docking-rescoring`.
17
18	## FEP Method Taxonomy
19
20	\| Method \| Cost / pair \| Accuracy \| Use case \| Fails when \|
21	\|--------\|-------------\|----------\|----------\|------------\|
22	\| FEP+ (Schrödinger) \| System- and protocol-dependent GPU cost \| Published commercial RBFE workflow \| Commercial lead optimization \| License and reproducibility constraints \|
23	\| OpenFE RBFE \| System- and protocol-dependent GPU cost \| Open-source RBFE with documented protocols \| Open-source campaigns \| Mapping/setup/sampling require review \|
24	\| OpenFE ABFE \| Generally more setup and sampling than one RBFE edge \| Standard binding free energy \| No congeneric reference ligand required \| Restraints and end-state corrections \|
25	\| GROMACS / AMBER RBFE \| Implementation-dependent \| Custom alchemical workflows \| Expert-controlled setup \| Manual validation burden \|
26	\| FEP-SPell-ABFE \| Protocol/system-dependent \| Automated ABFE workflow \| Evaluate published and project benchmarks \| Limited adoption \|
27	\| QligFEP v2.1 \| Protocol/system-dependent \| Q-based ligand FEP \| Evaluate published and project benchmarks \| Different approximations/tooling \|
28	\| MM/PBSA / MM/GBSA \| Lower-cost endpoint analysis \| Approximate endpoint score \| Exploratory within-series comparison \| Entropy, sampling, and model dependence \|
29	\| Boltz-2 affinity \| seconds GPU \| 0.66 Pearson on reported FEP benchmark subset \| ML alternative; reported >=1000x lower cost \| Novel chemotypes \|
30	\| ALEPB / EE-AMBER \| Protocol/system-dependent \| Specialized methods \| Evaluate matched evidence \| Limited tooling \|
31
32	Decision: For congeneric lead-optimization questions, evaluate a validated RBFE protocol and perturbation network. Use endpoint methods only for decisions supported by a project-specific benchmark. Candidate counts and escalation gates should follow compute budget, uncertainty, and prospective validation rather than a universal top-N rule.
33
34	## Decision Tree by Scenario
35
36	\| Scenario \| Recommended workflow \|
37	\|----------\|---------------------\|
38	\| Rank close analogs (R-group SAR) \| RBFE via OpenFE (cycle: lig1↔lig2↔lig3) \|
39	\| Cross-scaffold ranking \| ABFE per ligand; or coordinated RBFE with star network \|
40	\| Congeneric lead-optimization set \| RBFE with a connected, redundancy-aware perturbation graph \|
41	\| Single ligand affinity \| ABFE (no reference needed) \|
42	\| Lower-cost exploratory ranking \| A project-validated endpoint or ML method, followed by orthogonal confirmation \|
43	\| Novel scaffold prospective \| Treat ML affinity as triage; validate selected decisions prospectively \|
44	\| Selectivity (target vs off-target) \| RBFE on both proteins; report delta-delta-G \|
45	\| Allosteric vs orthosteric \| ABFE comparable; check pose stability with MD \|
46	\| Ions / metal centers \| Specialized force field (ZAFF, MCPB.py); not standard FEP \|
47
48	## Relative Binding Free Energy (RBFE) Setup
49
50	Goal: Calculate delta-delta-G between two ligands (lig1 -> lig2) in pocket.
51
52	Approach: Alchemical transformation lig1 -> lig2 in both bound state (pocket + ligand + water) and unbound state (ligand + water alone). Thermodynamic cycle:
53
54	```
55	delta(delta-G_binding) = (delt

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6 picks

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