Project for sharing surrogate models for TB inhibitors, data sharing, and Jupyter notebooks for prototyping.
The data directory contains data for training surrogate models, configurations and
results for redocking and MolPAL
based virtual screening. More details are listed in /data/README.md.
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0_raw/0_raw.tar.xz for data used for training machine leanring models. After uncompressing with
tar -xf 0_raw.tar.xz, the data will be stored in the0_rawfolder with the following structure:. ├── mlsmr │ ├── mlsmr_trn.csv │ ├── mlsmr_tst.csv │ └── mlsmr_val.csv ├── pk │ ├── pk_trn.csv │ ├── pk_tst.csv │ └── pk_val.csv └── taacf ├── taacf_trn.csv ├── taacf_tst.csv └── taacf_val.csv -
1_redocking docking validation data: PDB structures used for redocking, config files, images of docked poses, docking outputs.
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2_molpal for MolPAL related configurations and results.
As the project develops, environment.yml and requirements.txt can be used to keep python dependencies organized. This could turn into scripts and an installable python package later on.
The models directory contains trained surrogate models for TB inhibitors. More details
are listed in /models/README.md.
The notebooks directory contains Jupyter notebooks for pre-processing and
post-processing molecular library for molecular docking and virtual screening.