Skip to the rows

agentsclimarketplace

Binding kinetics

Skill Kdevos12/ALKYL/skills/binding-kinetics

Claude Plugin for CompChem , Drug Discovery & Organic Chemistry reasoning

Install

npx -y skills add Kdevos12/ALKYL --skill binding-kinetics

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

5 stars5 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Use when analyzing or predicting drug-target binding kinetics: kon, koff, KD, residence time, SPR data fitting (Langmuir/two-state), ITC thermodynamics, tau-RAMD and funnel metadynamics for unbinding, or kinetic QSAR models.

SKILL.md

2.9 KB, as published. Nobody here has run it

Binding Kinetics

Purpose

Analyze, predict, and optimize drug-target binding kinetics: on-rates (kon), off-rates (koff), residence time (RT = 1/koff), thermodynamic signatures (ΔH/ΔS), and structure-kinetics relationships (SKR).

When to Use This Skill

Analyzing SPR sensorgrams (Biacore/Sierra)
Fitting ITC thermograms for ΔH/ΔS/ΔG
Computing residence time from MD simulations (τRAMD, metadynamics)
Building QSAR models for koff/kon
Interpreting kinetic selectivity vs equilibrium selectivity
Prioritizing compounds by residence time, not just KD

Reference Files

File	Content
`references/kinetics-theory.md`	kon/koff/KD/RT definitions, kinetic selectivity, two-state binding, conformational selection vs induced fit, thermodynamic signatures
`references/spr-analysis.md`	SPR sensorgrams, 1:1 Langmuir fitting, two-state model, Rmax/Rtheor, bulk correction, Biacore data parsing, Python fitting
`references/itc-analysis.md`	ITC thermogram integration, n/KD/ΔH/ΔS/ΔG fitting, SEDPHAT equivalents in Python, van't Hoff, enthalpy-entropy compensation
`references/residence-time-md.md`	τRAMD (random acceleration MD), funnel metadynamics, WTmetaD koff estimation, HTMD τRAMD Python, unbinding pathway analysis
`references/kinetic-qsar.md`	Structure-kinetics relationships (SKR), features for koff/kon models, kinetic maps (LE vs kinetic efficiency), koff cliff detection

Quick Routing

"Fit my SPR data" → spr-analysis.md

"Fit my ITC experiment" → itc-analysis.md

"Compute residence time from MD" → residence-time-md.md

"Build a model to predict koff" → kinetic-qsar.md

"Why does my drug work despite poor KD?" → kinetics-theory.md

Key Relationships

# Core kinetic relationships
KD = koff / kon                    # M (equilibrium dissociation constant)
pKD = -log10(KD)                   # analogous to pIC50
RT = 1 / koff                      # seconds (residence time)
t_half = ln(2) / koff              # seconds (half-life of complex)

# Thermodynamics
ΔG = RT_gas * ln(KD)               # kcal/mol (RT_gas = 0.592 at 298K)
ΔG = ΔH - T*ΔS                    # enthalpy-entropy decomposition

# Typical drug ranges
# kon:  10^4 – 10^7 M^-1 s^-1
# koff: 10^-5 – 10^-1 s^-1
# KD:   nM – µM
# RT:   10 s – 10^5 s (hours)

Integration with ALKYL Skills

Docking poses for kinetic path analysis: docking skill
MD trajectories for τRAMD: force-fields skill + MDAnalysis
SKR feature computation: chem_props.py, chem_analyze.py
MMPA for koff SAR: mmpa skill
Uncertainty in kinetic predictions: uncertainty-qsar skill

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.