This is a trained Gradient-Boosting Trees model and accompanying python script from our paper "Design of Amine-Functionalized Materials for Direct Air Capture Using Integrated High-Throughput Calculations and Machine Learning" (https://arxiv.org/abs/2410.13982)
git clone https://github.com/lanl/binding_prediction.git
cd binding_prediction/binding_predictionconda create -n bind_pred -c conda-forge rdkit mordred numpy=1.23 scikit-learn=1.3.0
conda activate bind_pred
pip install rdchiralpython run_model.py smiles.txtThis will print a list of binding enthalpies for each SMILES in "smiles.txt" with the following format, one per line:
<parent SMILES>,<child SMILES>,<binding enthalpy>
If a SMILES has multiple binding sites (as described in the paper) then there will be a line for each unique child.
"smiles.txt" is provided as a small test case. Replace with your own text file (one SMILES per line) to run the model on other molecules.
Approved for open release (O4953).
© 2025. Triad National Security, LLC. All rights reserved. This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S. Department of Energy/National Nuclear Security Administration. All rights in the program are reserved by Triad National Security, LLC, and the U.S. Department of Energy/National Nuclear Security Administration. The Government is granted for itself and others acting on its behalf a nonexclusive, paid-up, irrevocable worldwide license in this material to reproduce, prepare derivative works, distribute copies to the public, perform publicly and display publicly, and to permit others to do so.
Copyright 2025
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