Code and data to study and predict drug-excipient co-aggregation for nanoparticle formation

For more information, please refer to the associated publications. https://www.nature.com/articles/s41565-021-00870-y

If you use this data or code, please kindly cite Reker et al. Nature Nanotechnology 2021 https://doi.org/10.1038/s41565-021-00870-y

Dependencies

The machine learning and the molecular simulations have been enabled through a broad range of available software packages. We have generated two distinct conda environments to enable the implementation of these two pipelines.

Machine Learning

runs in Python 2.7 using the scikit-learn library as well as the RDKit. Optional libraries from OpenPyXL and Joblib enable reading of self-aggregation data from the Shoichet lab as well as parallelization of code for more efficient processing (not implemented in this version of the repository). A fresh conda environment can be set up to run these experiments using

conda create -n python2 python=2.7 
conda activate python2 
conda install scikit-learn
conda install -c rdkit rdkit
conda install -c anaconda openpyxl
conda install -c anaconda joblib

Simulations

Dependent on Python libraries OpenMM, openmoltools, Open Babel, ambermini as a stripped down version of Amber, MDTraj, ParmEd, and RDKit. Also requires an installation of PACKMOL A fresh installation of Ubuntu 18 and anaconda can be set up to run these experiments using

conda create --name simulations
conda activate simulations
conda install -c omnia -c conda-forge openmm
python -m simtk.testInstallation
conda install -c openbabel openbabel
conda install -c kyleabeauchamp ambermini
conda install -c conda-forge mdtraj
conda install -c omnia parmed
conda install -c omnia openmoltools
conda install -c rdkit rdkit

sudo apt-get install gfortran
wget https://github.com/m3g/packmol/archive/20.010.tar.gz
tar -zxvf 20.010.tar.gz 
cd packmol-20.010 
./configure
make
export PATH="/home/user/packmol-20.010:$PATH"

Descriptions of folders

data

contains structural information of the used molecules and the screening results

machine learning

contains Python code to run predictions of self-aggregation and co-aggregation. All scripts also contain evaluations and bechmarks as well as adversarial control calculations.

simulations

contains code to run and automatically analyze short MD simulations to assess interaction potential of drug molecules and excipients