MultiDTA

dependencies

numpy == 1.19.4
kreas == 2.4.3
Pconsc4 == 0.4
pytorch == 1.7.0
PyG (torch-geometric) == 1.4.1
hhsuite (https://github.com/soedinglab/hh-suite)
rdkit == 2020.09.5
ccmpred (https://github.com/soedinglab/CCMpred)

data preparation

1. Prepare the data need for train. Get all msa files of the proteins in datasets (for more detail description of datasets, please refer to datasets), and using Pconsc4 to predict all the contact map. A script in the repo can be run to do all the steps:
   python scripts.py
   
   
2. And if you want to skip the long time preparation, please directly download the contact map and msa files which we already generated from files. For more detailed generating information, please refer to the "scripts.py". Then copy the corresponding two folders to each dataset dir. For example:
   (1) download the data.zip and unzip it.
   (2) copy two folders called "aln" and "pconsc4" from davis to the /data/davis of your repo, so do the KIBA.
   

train (cross validation)

5 folds cross validation.
python training_5folds.py 0 0 0
where the parameters are dataset selection, gpu selection, fold (0,1,2,3,4).

test

This is to do the prediction with the models we trained. And this step is to reproduce the experiments.
python test.py 0 0
and the parameters are dataset selection, gpu selection.