ALI RAZZAK MACHINE LEARNING SCRIPTS.
For "stroke_predictor_ml.py" to be run in Python3
This script used clinical data to predict stroke outcome.
For "system_residue_features.py" to be run in Python3
This script determines the energetic barrier of a ligand transition depending on kinetic and thermodynamic features of each residue. Residue scale predicition of ligand dynamic behaviour. Identify which residues are most responsive to the ligand. Intended to be used in CHARMM pipeline but can be more generally applied to other molecular dynamic pipelines.
For "umbrella_data.py" to be run in Python3
This script determines the energetic barrier of a ligand transition depending on kinetic and thermodynamic features of the protein and system. Intended to be used in CHARMM pipeline but can be more generally applied to other molecular dynamic pipelines.
For "unbiaed_system_feature_ml.py" to be run in Python3
This script determines the position of a ligand depending on various features of a molecular protein system. Intended to be used in CHARMM pipeline but can be more generally applied to other molecular dynamic pipelines. Several different data frames with different data.