Jupyter Notebook is available at : Batch Docking¶

Batch Docking Workflow with DockSuiteX¶

In [10]:

import sys 
import os
sys.path.insert(0, os.path.abspath('..')) 

from docksuitex.batch_docking import BatchProtein, BatchLigand, BatchGridCalculator, BatchVinaDocking, BatchAD4Docking
from docksuitex.utils import fetch_pdb, fetch_sdf, view_molecule, parse_vina_log, parse_ad4_dlg

import sys import os sys.path.insert(0, os.path.abspath('..')) from docksuitex.batch_docking import BatchProtein, BatchLigand, BatchGridCalculator, BatchVinaDocking, BatchAD4Docking from docksuitex.utils import fetch_pdb, fetch_sdf, view_molecule, parse_vina_log, parse_ad4_dlg

Fetch and Prepare Proteins¶

In [11]:

# Fetch multiple protein structures (1HVR, 2TRX) in parallel
fetch_pdb(pdbid = ["1HVR", "2TRX"], save_to="proteins", parallel=2)

# Fetch multiple protein structures (1HVR, 2TRX) in parallel fetch_pdb(pdbid = ["1HVR", "2TRX"], save_to="proteins", parallel=2)

✅ Downloaded 1HVR.pdb → C:\Users\Mangalam\Desktop\New folder (2)\DockSuiteX\examples\proteins\1HVR.pdb
✅ Downloaded 2TRX.pdb → C:\Users\Mangalam\Desktop\New folder (2)\DockSuiteX\examples\proteins\2TRX.pdb

Out[11]:

[WindowsPath('C:/Users/Mangalam/Desktop/New folder (2)/DockSuiteX/examples/proteins/1HVR.pdb'),
 WindowsPath('C:/Users/Mangalam/Desktop/New folder (2)/DockSuiteX/examples/proteins/2TRX.pdb')]

In [12]:

# Initialize BatchProtein to process all proteins in the 'proteins' directory
# This cleans the PDB, removes water/heterogens, and adds hydrogens/charges

batch_prot = BatchProtein(
    inputs="proteins",

    #Default parameters
    fix_pdb=True,
    remove_heterogens=True,
    remove_water=True,
    add_hydrogens=True,
    add_charges=True,
    preserve_charge_types=None, #eg: ["Zn", "Fe"]
)


# Run preparation for all proteins using 8 CPUs
batch_prot.prepare_all(save_to="prepared_proteins", cpu = 8)

# Initialize BatchProtein to process all proteins in the 'proteins' directory # This cleans the PDB, removes water/heterogens, and adds hydrogens/charges batch_prot = BatchProtein( inputs="proteins", #Default parameters fix_pdb=True, remove_heterogens=True, remove_water=True, add_hydrogens=True, add_charges=True, preserve_charge_types=None, #eg: ["Zn", "Fe"] ) # Run preparation for all proteins using 8 CPUs batch_prot.prepare_all(save_to="prepared_proteins", cpu = 8)

Starting protein preparation for 2 files...
Using 2 parallel workers, 1 CPUs per worker
Output directory: C:\Users\Mangalam\Desktop\New folder (2)\DockSuiteX\examples\prepared_proteins
✅ Batch processing completed!

Out[12]:

[{'file': 'C:\\Users\\Mangalam\\Desktop\\New folder (2)\\DockSuiteX\\examples\\proteins\\1HVR.pdb',
  'status': 'success',
  'pdbqt_path': 'C:\\Users\\Mangalam\\Desktop\\New folder (2)\\DockSuiteX\\examples\\prepared_proteins\\1HVR.pdbqt'},
 {'file': 'C:\\Users\\Mangalam\\Desktop\\New folder (2)\\DockSuiteX\\examples\\proteins\\2TRX.pdb',
  'status': 'success',
  'pdbqt_path': 'C:\\Users\\Mangalam\\Desktop\\New folder (2)\\DockSuiteX\\examples\\prepared_proteins\\2TRX.pdbqt'}]

Find Binding Pockets¶

In [13]:

# Identify binding pockets for all prepared proteins using P2Rank
batch_gc = BatchGridCalculator(inputs="prepared_proteins",  max_pockets=3, mode="p2rank")

# Run pocket detection and return receptors with their pocket centers
grid_results = batch_gc.run_all(save_to="p2rank_outputs", cpu=8)

# Identify binding pockets for all prepared proteins using P2Rank batch_gc = BatchGridCalculator(inputs="prepared_proteins", max_pockets=3, mode="p2rank") # Run pocket detection and return receptors with their pocket centers grid_results = batch_gc.run_all(save_to="p2rank_outputs", cpu=8)

Starting batch grid calculation (p2rank mode)
Using 2 parallel workers, 4 CPUs per worker
Output directory: C:\Users\Mangalam\Desktop\New folder (2)\DockSuiteX\examples\p2rank_outputs
✅ 1HVR.pdbqt  →  2 pocket(s)
[{'center': (-9.2421, 16.1006, 27.6026),
  'grid_size': (35.032, 33.316, 39.992),
  'probability': 0.929,
  'rank': 1},
 {'center': (-12.5447, 21.7728, 45.5697),
  'grid_size': (20.891, 24.788, 25.599),
  'probability': 0.012,
  'rank': 2}]
✅ 2TRX.pdbqt  →  3 pocket(s)
[{'center': (24.8541, 31.8383, 10.2135),
  'grid_size': (23.651, 22.617, 27.608),
  'probability': 0.132,
  'rank': 1},
 {'center': (12.529, 36.7841, 19.8122),
  'grid_size': (23.751, 23.242, 29.613),
  'probability': 0.105,
  'rank': 2},
 {'center': (25.6872, 31.3306, -7.9765),
  'grid_size': (23.39, 23.791, 28.063),
  'probability': 0.037,
  'rank': 3}]
✅ Batch grid calculation completed.

2. Fetch and Prepare Ligands¶

In [14]:

# Fetch multiple ligand structures in parallel
fetch_sdf(molecule_id=["228", "338"], save_to="ligands", parallel=2)

# Fetch multiple ligand structures in parallel fetch_sdf(molecule_id=["228", "338"], save_to="ligands", parallel=2)

✅ Downloaded 228.sdf (PubChem) → C:\Users\Mangalam\Desktop\New folder (2)\DockSuiteX\examples\ligands\228.sdf
✅ Downloaded 338.sdf (PubChem) → C:\Users\Mangalam\Desktop\New folder (2)\DockSuiteX\examples\ligands\338.sdf

Out[14]:

[WindowsPath('C:/Users/Mangalam/Desktop/New folder (2)/DockSuiteX/examples/ligands/228.sdf'),
 WindowsPath('C:/Users/Mangalam/Desktop/New folder (2)/DockSuiteX/examples/ligands/338.sdf')]

In [15]:

# Initialize BatchLigand to process all ligands in the 'ligands' directory
# This minimizes the structure, removes water and adds hydrogens/charges

batch_lig = BatchLigand(
    inputs="ligands",

    #Default parameters
    minimize=None,  #Options: "mmff94", "mmff94s", "uff", "gaff"
    remove_water=True,
    add_hydrogens=True,
    add_charges=True,
    preserve_charge_types=None,
)
# Run preparation for all ligands using 8 CPUs
batch_lig.prepare_all(save_to="prepared_ligands", cpu=8)

# Initialize BatchLigand to process all ligands in the 'ligands' directory # This minimizes the structure, removes water and adds hydrogens/charges batch_lig = BatchLigand( inputs="ligands", #Default parameters minimize=None, #Options: "mmff94", "mmff94s", "uff", "gaff" remove_water=True, add_hydrogens=True, add_charges=True, preserve_charge_types=None, ) # Run preparation for all ligands using 8 CPUs batch_lig.prepare_all(save_to="prepared_ligands", cpu=8)

Starting ligand preparation for 2 files...
Using 2 parallel workers, 1 CPUs per worker
Output directory: C:\Users\Mangalam\Desktop\New folder (2)\DockSuiteX\examples\prepared_ligands
✅ Batch processing completed!

Out[15]:

[{'file': 'C:\\Users\\Mangalam\\Desktop\\New folder (2)\\DockSuiteX\\examples\\ligands\\338.sdf',
  'status': 'success',
  'pdbqt_path': 'C:\\Users\\Mangalam\\Desktop\\New folder (2)\\DockSuiteX\\examples\\prepared_ligands\\338.pdbqt'},
 {'file': 'C:\\Users\\Mangalam\\Desktop\\New folder (2)\\DockSuiteX\\examples\\ligands\\228.sdf',
  'status': 'success',
  'pdbqt_path': 'C:\\Users\\Mangalam\\Desktop\\New folder (2)\\DockSuiteX\\examples\\prepared_ligands\\228.pdbqt'}]

Dock each ligand against a each receptor at all the predicted protein pockets¶

Using AutoDock Vina¶

In [16]:

# Initialize BatchVinaDocking with prepared receptors (and their pockets) and ligands
batch_vina = BatchVinaDocking(
    receptors_with_pockets=grid_results,
    ligands="prepared_ligands",
    seed=42, # Random seed for reproducibility

    # Default Parameters
    exhaustiveness=8,
    num_modes=9, 
)

# Run Vina docking for all receptor-ligand pairs using 8 CPUs
batch_vina.run_all(save_to="vina_batch_results", cpu=8)

# Initialize BatchVinaDocking with prepared receptors (and their pockets) and ligands batch_vina = BatchVinaDocking( receptors_with_pockets=grid_results, ligands="prepared_ligands", seed=42, # Random seed for reproducibility # Default Parameters exhaustiveness=8, num_modes=9, ) # Run Vina docking for all receptor-ligand pairs using 8 CPUs batch_vina.run_all(save_to="vina_batch_results", cpu=8)

Starting AutoDock Vina docking for 10 tasks...
Using 8 parallel workers, 1 CPUs per worker
Output directory: C:\Users\Mangalam\Desktop\New folder (2)\DockSuiteX\examples\vina_batch_results
✅ Batch processing completed!

Out[16]:

{('1HVR.pdbqt',
  '338.pdbqt',
  (-12.5447,
   21.7728,
   45.5697)): WindowsPath('C:/Users/Mangalam/Desktop/New folder (2)/DockSuiteX/examples/vina_batch_results/1HVR_338_center_-12.54_21.77_45.57'),
 ('2TRX.pdbqt',
  '338.pdbqt',
  (24.8541,
   31.8383,
   10.2135)): WindowsPath('C:/Users/Mangalam/Desktop/New folder (2)/DockSuiteX/examples/vina_batch_results/2TRX_338_center_24.85_31.84_10.21'),
 ('2TRX.pdbqt',
  '338.pdbqt',
  (12.529,
   36.7841,
   19.8122)): WindowsPath('C:/Users/Mangalam/Desktop/New folder (2)/DockSuiteX/examples/vina_batch_results/2TRX_338_center_12.53_36.78_19.81'),
 ('1HVR.pdbqt',
  '338.pdbqt',
  (-9.2421,
   16.1006,
   27.6026)): WindowsPath('C:/Users/Mangalam/Desktop/New folder (2)/DockSuiteX/examples/vina_batch_results/1HVR_338_center_-9.24_16.10_27.60'),
 ('2TRX.pdbqt',
  '338.pdbqt',
  (25.6872,
   31.3306,
   -7.9765)): WindowsPath('C:/Users/Mangalam/Desktop/New folder (2)/DockSuiteX/examples/vina_batch_results/2TRX_338_center_25.69_31.33_-7.98'),
 ('1HVR.pdbqt',
  '228.pdbqt',
  (-9.2421,
   16.1006,
   27.6026)): WindowsPath('C:/Users/Mangalam/Desktop/New folder (2)/DockSuiteX/examples/vina_batch_results/1HVR_228_center_-9.24_16.10_27.60'),
 ('1HVR.pdbqt',
  '228.pdbqt',
  (-12.5447,
   21.7728,
   45.5697)): WindowsPath('C:/Users/Mangalam/Desktop/New folder (2)/DockSuiteX/examples/vina_batch_results/1HVR_228_center_-12.54_21.77_45.57'),
 ('2TRX.pdbqt',
  '228.pdbqt',
  (24.8541,
   31.8383,
   10.2135)): WindowsPath('C:/Users/Mangalam/Desktop/New folder (2)/DockSuiteX/examples/vina_batch_results/2TRX_228_center_24.85_31.84_10.21'),
 ('2TRX.pdbqt',
  '228.pdbqt',
  (25.6872,
   31.3306,
   -7.9765)): WindowsPath('C:/Users/Mangalam/Desktop/New folder (2)/DockSuiteX/examples/vina_batch_results/2TRX_228_center_25.69_31.33_-7.98'),
 ('2TRX.pdbqt',
  '228.pdbqt',
  (12.529,
   36.7841,
   19.8122)): WindowsPath('C:/Users/Mangalam/Desktop/New folder (2)/DockSuiteX/examples/vina_batch_results/2TRX_228_center_12.53_36.78_19.81')}

In [17]:

# Parse all Vina output logs into a single CSV summary
batch_vina.parse_results()

# Parse all Vina output logs into a single CSV summary batch_vina.parse_results()

Starting Vina log parsing for 10 file(s)...
✅ Parsed C:\Users\Mangalam\Desktop\New folder (2)\DockSuiteX\examples\vina_batch_results\1HVR_338_center_-12.54_21.77_45.57\log.txt → 9 modes
✅ Parsed C:\Users\Mangalam\Desktop\New folder (2)\DockSuiteX\examples\vina_batch_results\2TRX_338_center_24.85_31.84_10.21\log.txt → 9 modes
✅ Parsed C:\Users\Mangalam\Desktop\New folder (2)\DockSuiteX\examples\vina_batch_results\2TRX_338_center_12.53_36.78_19.81\log.txt → 9 modes
✅ Parsed C:\Users\Mangalam\Desktop\New folder (2)\DockSuiteX\examples\vina_batch_results\1HVR_338_center_-9.24_16.10_27.60\log.txt → 9 modes
✅ Parsed C:\Users\Mangalam\Desktop\New folder (2)\DockSuiteX\examples\vina_batch_results\2TRX_338_center_25.69_31.33_-7.98\log.txt → 9 modes
✅ Parsed C:\Users\Mangalam\Desktop\New folder (2)\DockSuiteX\examples\vina_batch_results\1HVR_228_center_-9.24_16.10_27.60\log.txt → 9 modes
✅ Parsed C:\Users\Mangalam\Desktop\New folder (2)\DockSuiteX\examples\vina_batch_results\1HVR_228_center_-12.54_21.77_45.57\log.txt → 9 modes
✅ Parsed C:\Users\Mangalam\Desktop\New folder (2)\DockSuiteX\examples\vina_batch_results\2TRX_228_center_24.85_31.84_10.21\log.txt → 9 modes
✅ Parsed C:\Users\Mangalam\Desktop\New folder (2)\DockSuiteX\examples\vina_batch_results\2TRX_228_center_25.69_31.33_-7.98\log.txt → 9 modes
✅ Parsed C:\Users\Mangalam\Desktop\New folder (2)\DockSuiteX\examples\vina_batch_results\2TRX_228_center_12.53_36.78_19.81\log.txt → 9 modes
✅ Parsing completed! Saved 90 results to C:\Users\Mangalam\Desktop\New folder (2)\DockSuiteX\examples\vina_batch_results\vina_summary.csv

Out[17]:

	Receptor	Ligand	Grid Center X	Grid Center Y	Grid Center Z	Grid Size X	Grid Size Y	Grid Size Z	Grid Spacing	Exhaustiveness	Mode	Affinity (kcal/mol)	RMSD LB	RMSD UB
0	1HVR	338	-12.5447	21.7728	45.5697	20.891	24.788	25.599	0.375	8	1	-4.794	0.000	0.000
1	1HVR	338	-12.5447	21.7728	45.5697	20.891	24.788	25.599	0.375	8	2	-4.759	1.483	2.811
2	1HVR	338	-12.5447	21.7728	45.5697	20.891	24.788	25.599	0.375	8	3	-4.713	10.160	11.160
3	1HVR	338	-12.5447	21.7728	45.5697	20.891	24.788	25.599	0.375	8	4	-4.442	9.782	11.020
4	1HVR	338	-12.5447	21.7728	45.5697	20.891	24.788	25.599	0.375	8	5	-4.251	9.836	11.120
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
85	2TRX	228	12.5290	36.7841	19.8122	23.751	23.242	29.613	0.375	8	5	-5.130	5.868	8.998
86	2TRX	228	12.5290	36.7841	19.8122	23.751	23.242	29.613	0.375	8	6	-5.014	6.644	8.873
87	2TRX	228	12.5290	36.7841	19.8122	23.751	23.242	29.613	0.375	8	7	-4.955	5.225	8.597
88	2TRX	228	12.5290	36.7841	19.8122	23.751	23.242	29.613	0.375	8	8	-4.945	16.450	17.700
89	2TRX	228	12.5290	36.7841	19.8122	23.751	23.242	29.613	0.375	8	9	-4.621	9.793	12.190

90 rows × 14 columns

In [18]:

# Generate ProLIF interaction fingerprints for all successful docking results.
# This identifies hydrogen bonds, hydrophobic contacts, etc.
batch_vina.interaction_profile(cpu=2)

# Generate ProLIF interaction fingerprints for all successful docking results. # This identifies hydrogen bonds, hydrophobic contacts, etc. batch_vina.interaction_profile(cpu=2)

Starting ProLIF interaction profiling for 10 results...
Using 2 parallel workers, 1 CPUs per worker
✅ Batch interaction profiling completed!
{('1HVR.pdbqt', '228.pdbqt', (-12.5447, 21.7728, 45.5697)): WindowsPath('C:/Users/Mangalam/Desktop/New folder (2)/DockSuiteX/examples/vina_batch_results/1HVR_228_center_-12.54_21.77_45.57/prolif_results'),
 ('1HVR.pdbqt', '228.pdbqt', (-9.2421, 16.1006, 27.6026)): WindowsPath('C:/Users/Mangalam/Desktop/New folder (2)/DockSuiteX/examples/vina_batch_results/1HVR_228_center_-9.24_16.10_27.60/prolif_results'),
 ('1HVR.pdbqt', '338.pdbqt', (-12.5447, 21.7728, 45.5697)): WindowsPath('C:/Users/Mangalam/Desktop/New folder (2)/DockSuiteX/examples/vina_batch_results/1HVR_338_center_-12.54_21.77_45.57/prolif_results'),
 ('1HVR.pdbqt', '338.pdbqt', (-9.2421, 16.1006, 27.6026)): WindowsPath('C:/Users/Mangalam/Desktop/New folder (2)/DockSuiteX/examples/vina_batch_results/1HVR_338_center_-9.24_16.10_27.60/prolif_results'),
 ('2TRX.pdbqt', '228.pdbqt', (12.529, 36.7841, 19.8122)): WindowsPath('C:/Users/Mangalam/Desktop/New folder (2)/DockSuiteX/examples/vina_batch_results/2TRX_228_center_12.53_36.78_19.81/prolif_results'),
 ('2TRX.pdbqt', '228.pdbqt', (24.8541, 31.8383, 10.2135)): WindowsPath('C:/Users/Mangalam/Desktop/New folder (2)/DockSuiteX/examples/vina_batch_results/2TRX_228_center_24.85_31.84_10.21/prolif_results'),
 ('2TRX.pdbqt', '228.pdbqt', (25.6872, 31.3306, -7.9765)): WindowsPath('C:/Users/Mangalam/Desktop/New folder (2)/DockSuiteX/examples/vina_batch_results/2TRX_228_center_25.69_31.33_-7.98/prolif_results'),
 ('2TRX.pdbqt', '338.pdbqt', (12.529, 36.7841, 19.8122)): WindowsPath('C:/Users/Mangalam/Desktop/New folder (2)/DockSuiteX/examples/vina_batch_results/2TRX_338_center_12.53_36.78_19.81/prolif_results'),
 ('2TRX.pdbqt', '338.pdbqt', (24.8541, 31.8383, 10.2135)): WindowsPath('C:/Users/Mangalam/Desktop/New folder (2)/DockSuiteX/examples/vina_batch_results/2TRX_338_center_24.85_31.84_10.21/prolif_results'),
 ('2TRX.pdbqt', '338.pdbqt', (25.6872, 31.3306, -7.9765)): WindowsPath('C:/Users/Mangalam/Desktop/New folder (2)/DockSuiteX/examples/vina_batch_results/2TRX_338_center_25.69_31.33_-7.98/prolif_results')}
✅ Combined ProLIF CSV saved to: C:\Users\Mangalam\Desktop\New folder (2)\DockSuiteX\examples\vina_batch_results\prolif_interactions_combined.csv

Out[18]:

ligand	Info				UNL1
protein	Receptor	Ligand	Center	Pose	ASP15.0		THR54.0	ALA93.1	HOH404.2	HOH412.2	...	SER11.1	TRP28.1	GLU30.1		LYS36.1	ASN63.1	HOH434.3	HOH454.3	HOH497.3	HOH505.3
interaction					HBDonor	VdWContact	VdWContact	VdWContact	VdWContact	VdWContact	...	VdWContact	VdWContact	HBDonor	VdWContact	VdWContact	VdWContact	VdWContact	VdWContact	VdWContact	VdWContact
0	2TRX.pdbqt	338.pdbqt	24.85_31.84_10.21	1	False	False	False	True	False	False	...	False	False	False	False	False	False	False	False	False	False
1	2TRX.pdbqt	338.pdbqt	24.85_31.84_10.21	2	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
2	2TRX.pdbqt	338.pdbqt	24.85_31.84_10.21	3	False	False	True	False	True	False	...	False	False	False	False	False	False	False	False	False	False
3	2TRX.pdbqt	338.pdbqt	24.85_31.84_10.21	4	False	False	False	True	False	False	...	False	False	False	False	False	False	False	False	False	False
4	2TRX.pdbqt	338.pdbqt	24.85_31.84_10.21	5	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
85	2TRX.pdbqt	228.pdbqt	12.53_36.78_19.81	5	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
86	2TRX.pdbqt	228.pdbqt	12.53_36.78_19.81	6	False	False	False	False	False	False	...	False	False	False	False	False	False	True	True	False	True
87	2TRX.pdbqt	228.pdbqt	12.53_36.78_19.81	7	False	False	False	False	False	False	...	False	True	True	True	False	True	False	True	False	False
88	2TRX.pdbqt	228.pdbqt	12.53_36.78_19.81	8	False	True	False	False	False	False	...	False	False	False	False	True	False	False	False	False	False
89	2TRX.pdbqt	228.pdbqt	12.53_36.78_19.81	9	False	False	False	False	False	False	...	True	False	False	False	False	False	True	False	True	True

90 rows × 130 columns

Using AutoDock4¶

In [ ]:

# Initialize BatchAD4Docking with prepared receptors (and their pockets) and ligands
batch_ad4 = BatchAD4Docking(
    receptors_with_centers=grid_results,
    ligands="prepared_ligands",
    seed=(21, 42),  #Random seed for reproducibility

    #Default Parameters
    grid_size=(60,60,60),
    spacing=0.375,     
    dielectric=-0.1465, 
    smooth=0.5,          
    ga_pop_size=150,
    ga_num_evals=2500000,
    ga_num_generations=27000,
    ga_elitism=1,
    ga_mutation_rate=0.02,
    ga_crossover_rate=0.8,
    ga_run=10,
    rmstol=2.0,
)

# Run AutoDock4 docking for all receptor-ligand pairs using 8 CPUs
results_ad4 = batch_ad4.run_all(save_to="ad4_batch_results", cpu=8)

# Initialize BatchAD4Docking with prepared receptors (and their pockets) and ligands batch_ad4 = BatchAD4Docking( receptors_with_centers=grid_results, ligands="prepared_ligands", seed=(21, 42), #Random seed for reproducibility #Default Parameters grid_size=(60,60,60), spacing=0.375, dielectric=-0.1465, smooth=0.5, ga_pop_size=150, ga_num_evals=2500000, ga_num_generations=27000, ga_elitism=1, ga_mutation_rate=0.02, ga_crossover_rate=0.8, ga_run=10, rmstol=2.0, ) # Run AutoDock4 docking for all receptor-ligand pairs using 8 CPUs results_ad4 = batch_ad4.run_all(save_to="ad4_batch_results", cpu=8)

In [ ]:

# Parse all AD4 output logs into a single CSV summary
batch_ad4.parse_results()

# Parse all AD4 output logs into a single CSV summary batch_ad4.parse_results()

In [ ]:

# Generate ProLIF interaction fingerprints for all successful docking results.
# This identifies hydrogen bonds, hydrophobic contacts, etc.
batch_ad4.interaction_profile()

# Generate ProLIF interaction fingerprints for all successful docking results. # This identifies hydrogen bonds, hydrophobic contacts, etc. batch_ad4.interaction_profile()