bindtools 0.1.2

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bindtools

bindtools is a Python library for supramolecular chemistry designed for modeling, fitting, and analyzing binding equilibria. It provides numerical and analytical solvers for complex chemical systems, robust parameter optimization, and Bayesian parameter estimation.

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Features

• Speciation Solvers:
  • Numerical: A Newton-Raphson solver (DoNR and getConcs) JIT-compiled with numba for fast operation.
  • Analytical: High-speed analytical solvers for common topologies (e.g., 1:1, 1:2, 2:1 fast exchange).
• Flexible Optimization:
  • Uses lmfit to manage model parameters (binding constants, physical observables).
  • Handles various experimental data including NMR integrations, chemical shifts (NMR deltaH/deltaF), and linear concentration-weighted observables (UV-vis / fluorescence).
• Bayesian Inference & Uncertainty Quantification:
  • Uses emcee for Markov Chain Monte Carlo (MCMC) sampling.
  • Generates trace/chain convergence plots and corner plots using corner and arviz.
  • MCMC runs can be serialized and stored as HDF5 files for future analysis.

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Installation

bindtools is available on PyPI. You can install it directly using pip:

pip install bindtools

Using Conda / Mamba (Recommended for Virtual Environments)

To avoid dependency conflicts, you can set up a dedicated environment with Conda/Mamba and install bindtools inside it:

# 1. Create and configure environment with base scientific dependencies
mamba create -n binding -c conda-forge \
  python jupyter tqdm ipython uncertainties lmfit scipy numpy emcee tqdm numba corner matplotlib numdifftools

# 2. Activate the environment
conda activate binding

# 3. Install bindtools via pip
pip install bindtools

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Quick Start

1. Speciation (Solving Concentration Problems)

You can compute the equilibrium concentration of free species (components and complexes) given initial total concentrations, a stoichiometry matrix, and equilibrium constants ($K$ values).

import numpy as np
from bindtools import binding as bd

# Define total concentrations: 50 data points of Host (1e-3 M) and Guest (0 to 1e-2 M)
component_concs = np.zeros((50, 2))
component_concs[:, 0] = 1e-3
component_concs[:, 1] = np.linspace(0, 1e-2, 50)

# Stoichiometry / Equilibrium Matrix
# Row 0: Host balance, Row 1: Guest balance
# Columns represent: [Free Host, Free Guest, Host-Guest Complex (1:1)]
eq_mat = np.array([
    [1, 0, 1],  # [H]_tot = [H] + [HG]
    [0, 1, 1]   # [G]_tot = [G] + [HG]
])

# log10(K) values for each species. 
# Constants for free components are fixed at logK = 0.
# The complex (HG) has logK = 4 (K = 10,000 M^-1).
logK = np.array([0, 0, 4])

# Solve for concentrations at each point
results = []
for total_concs in component_concs:
    spec_concs = bd.getConcs(eq_mat, total_concs, logK)
    results.append(spec_concs)

results = np.array(results)
print("First point [H, G, HG]:", results[0])

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License

This project is licensed under the MIT License - see the LICENSE file for details.