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process_bindingmoad.py
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process_bindingmoad.py
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from pathlib import Path
from time import time
import random
from collections import defaultdict
import argparse
import warnings
from tqdm import tqdm
import numpy as np
import torch
from Bio.PDB import PDBParser
from Bio.PDB.Polypeptide import three_to_one, is_aa
from Bio.PDB import PDBIO, Select
from openbabel import openbabel
from rdkit import Chem
from rdkit.Chem import QED
from scipy.ndimage import gaussian_filter
from geometry_utils import get_bb_transform
from analysis.molecule_builder import build_molecule
from analysis.metrics import rdmol_to_smiles
import constants
from constants import covalent_radii, dataset_params
import utils
dataset_info = dataset_params['bindingmoad']
amino_acid_dict = dataset_info['aa_encoder']
atom_dict = dataset_info['atom_encoder']
atom_decoder = dataset_info['atom_decoder']
class Model0(Select):
def accept_model(self, model):
return model.id == 0
def read_label_file(csv_path):
"""
Read BindingMOAD's label file
Args:
csv_path: path to 'every.csv'
Returns:
Nested dictionary with all ligands. First level: EC number,
Second level: PDB ID, Third level: list of ligands. Each ligand is
represented as a tuple (ligand name, validity, SMILES string)
"""
ligand_dict = {}
with open(csv_path, 'r') as f:
for line in f.readlines():
row = line.split(',')
# new protein class
if len(row[0]) > 0:
curr_class = row[0]
ligand_dict[curr_class] = {}
continue
# new protein
if len(row[2]) > 0:
curr_prot = row[2]
ligand_dict[curr_class][curr_prot] = []
continue
# new small molecule
if len(row[3]) > 0:
ligand_dict[curr_class][curr_prot].append(
# (ligand name, validity, SMILES string)
[row[3], row[4], row[9]]
)
return ligand_dict
def compute_druglikeness(ligand_dict):
"""
Computes RDKit's QED value and adds it to the dictionary
Args:
ligand_dict: nested ligand dictionary
Returns:
the same ligand dictionary with additional QED values
"""
print("Computing QED values...")
for p, m in tqdm([(p, m) for c in ligand_dict for p in ligand_dict[c]
for m in ligand_dict[c][p]]):
mol = Chem.MolFromSmiles(m[2])
if mol is None:
mol_id = f'{p}_{m}'
warnings.warn(f"Could not construct molecule {mol_id} from SMILES "
f"string '{m[2]}'")
continue
m.append(QED.qed(mol))
return ligand_dict
def filter_and_flatten(ligand_dict, qed_thresh, max_occurences, seed):
filtered_examples = []
all_examples = [(c, p, m) for c in ligand_dict for p in ligand_dict[c]
for m in ligand_dict[c][p]]
# shuffle to select random examples of ligands that occur more than
# max_occurences times
random.seed(seed)
random.shuffle(all_examples)
ligand_name_counter = defaultdict(int)
print("Filtering examples...")
for c, p, m in tqdm(all_examples):
ligand_name, ligand_chain, ligand_resi = m[0].split(':')
if m[1] == 'valid' and len(m) > 3 and m[3] > qed_thresh:
if ligand_name_counter[ligand_name] < max_occurences:
filtered_examples.append(
(c, p, m)
)
ligand_name_counter[ligand_name] += 1
return filtered_examples
def split_by_ec_number(data_list, n_val, n_test, ec_level=1):
"""
Split dataset into training, validation and test sets based on EC numbers
https://en.wikipedia.org/wiki/Enzyme_Commission_number
Args:
data_list: list of ligands
n_val: number of validation examples
n_test: number of test examples
ec_level: level in the EC numbering hierarchy at which the split is
made, i.e. items with matching EC numbers at this level are put in
the same set
Returns:
dictionary with keys 'train', 'val', and 'test'
"""
examples_per_class = defaultdict(int)
for c, p, m in data_list:
c_sub = '.'.join(c.split('.')[:ec_level])
examples_per_class[c_sub] += 1
assert sum(examples_per_class.values()) == len(data_list)
# split ec numbers
val_classes = set()
for c, num in sorted(examples_per_class.items(), key=lambda x: x[1],
reverse=True):
if sum([examples_per_class[x] for x in val_classes]) + num <= n_val:
val_classes.add(c)
test_classes = set()
for c, num in sorted(examples_per_class.items(), key=lambda x: x[1],
reverse=True):
# skip classes already used in the validation set
if c in val_classes:
continue
if sum([examples_per_class[x] for x in test_classes]) + num <= n_test:
test_classes.add(c)
# remaining classes belong to test set
train_classes = {x for x in examples_per_class if
x not in val_classes and x not in test_classes}
# create separate lists of examples
data_split = {}
data_split['train'] = [x for x in data_list if '.'.join(
x[0].split('.')[:ec_level]) in train_classes]
data_split['val'] = [x for x in data_list if '.'.join(
x[0].split('.')[:ec_level]) in val_classes]
data_split['test'] = [x for x in data_list if '.'.join(
x[0].split('.')[:ec_level]) in test_classes]
assert len(data_split['train']) + len(data_split['val']) + \
len(data_split['test']) == len(data_list)
return data_split
def ligand_list_to_dict(ligand_list):
out_dict = defaultdict(list)
for _, p, m in ligand_list:
out_dict[p].append(m)
return out_dict
def process_ligand_and_pocket(pdb_struct, ligand_name, ligand_chain,
ligand_resi, dist_cutoff, ca_only,
compute_quaternion=False):
try:
residues = {obj.id[1]: obj for obj in
pdb_struct[0][ligand_chain].get_residues()}
except KeyError as e:
raise KeyError(f'Chain {e} not found ({pdbfile}, '
f'{ligand_name}:{ligand_chain}:{ligand_resi})')
ligand = residues[ligand_resi]
assert ligand.get_resname() == ligand_name, \
f"{ligand.get_resname()} != {ligand_name}"
# remove H atoms if not in atom_dict, other atom types that aren't allowed
# should stay so that the entire ligand can be removed from the dataset
lig_atoms = [a for a in ligand.get_atoms()
if (a.element.capitalize() in atom_dict or a.element != 'H')]
lig_coords = np.array([a.get_coord() for a in lig_atoms])
try:
lig_one_hot = np.stack([
np.eye(1, len(atom_dict), atom_dict[a.element.capitalize()]).squeeze()
for a in lig_atoms
])
except KeyError as e:
raise KeyError(
f'Ligand atom {e} not in atom dict ({pdbfile}, '
f'{ligand_name}:{ligand_chain}:{ligand_resi})')
# Find interacting pocket residues based on distance cutoff
pocket_residues = []
for residue in pdb_struct[0].get_residues():
res_coords = np.array([a.get_coord() for a in residue.get_atoms()])
if is_aa(residue.get_resname(), standard=True) and \
(((res_coords[:, None, :] - lig_coords[None, :, :]) ** 2).sum(-1) ** 0.5).min() < dist_cutoff:
pocket_residues.append(residue)
# Compute transform of the canonical reference frame
n_xyz = np.array([res['N'].get_coord() for res in pocket_residues])
ca_xyz = np.array([res['CA'].get_coord() for res in pocket_residues])
c_xyz = np.array([res['C'].get_coord() for res in pocket_residues])
if compute_quaternion:
quaternion, c_alpha = get_bb_transform(n_xyz, ca_xyz, c_xyz)
if np.any(np.isnan(quaternion)):
raise ValueError(
f'Invalid value in quaternion ({pdbfile}, '
f'{ligand_name}:{ligand_chain}:{ligand_resi})')
else:
c_alpha = ca_xyz
if ca_only:
pocket_coords = c_alpha
try:
pocket_one_hot = np.stack([
np.eye(1, len(amino_acid_dict),
amino_acid_dict[three_to_one(res.get_resname())]).squeeze()
for res in pocket_residues])
except KeyError as e:
raise KeyError(
f'{e} not in amino acid dict ({pdbfile}, '
f'{ligand_name}:{ligand_chain}:{ligand_resi})')
else:
pocket_atoms = [a for res in pocket_residues for a in res.get_atoms()
if (a.element.capitalize() in atom_dict or a.element != 'H')]
pocket_coords = np.array([a.get_coord() for a in pocket_atoms])
try:
pocket_one_hot = np.stack([
np.eye(1, len(atom_dict), atom_dict[a.element.capitalize()]).squeeze()
for a in pocket_atoms
])
except KeyError as e:
raise KeyError(
f'Pocket atom {e} not in atom dict ({pdbfile}, '
f'{ligand_name}:{ligand_chain}:{ligand_resi})')
pocket_ids = [f'{res.parent.id}:{res.id[1]}' for res in pocket_residues]
ligand_data = {
'lig_coords': lig_coords,
'lig_one_hot': lig_one_hot,
}
pocket_data = {
'pocket_coords': pocket_coords,
'pocket_one_hot': pocket_one_hot,
'pocket_ids': pocket_ids,
}
if compute_quaternion:
pocket_data['pocket_quaternion'] = quaternion
return ligand_data, pocket_data
def compute_smiles(positions, one_hot, mask):
print("Computing SMILES ...")
atom_types = np.argmax(one_hot, axis=-1)
sections = np.where(np.diff(mask))[0] + 1
positions = [torch.from_numpy(x) for x in np.split(positions, sections)]
atom_types = [torch.from_numpy(x) for x in np.split(atom_types, sections)]
mols_smiles = []
pbar = tqdm(enumerate(zip(positions, atom_types)),
total=len(np.unique(mask)))
for i, (pos, atom_type) in pbar:
mol = build_molecule(pos, atom_type, dataset_info)
# BasicMolecularMetrics() computes SMILES after sanitization
try:
Chem.SanitizeMol(mol)
except ValueError:
continue
mol = rdmol_to_smiles(mol)
if mol is not None:
mols_smiles.append(mol)
pbar.set_description(f'{len(mols_smiles)}/{i + 1} successful')
return mols_smiles
def get_n_nodes(lig_mask, pocket_mask, smooth_sigma=None):
# Joint distribution of ligand's and pocket's number of nodes
idx_lig, n_nodes_lig = np.unique(lig_mask, return_counts=True)
idx_pocket, n_nodes_pocket = np.unique(pocket_mask, return_counts=True)
assert np.all(idx_lig == idx_pocket)
joint_histogram = np.zeros((np.max(n_nodes_lig) + 1,
np.max(n_nodes_pocket) + 1))
for nlig, npocket in zip(n_nodes_lig, n_nodes_pocket):
joint_histogram[nlig, npocket] += 1
print(f'Original histogram: {np.count_nonzero(joint_histogram)}/'
f'{joint_histogram.shape[0] * joint_histogram.shape[1]} bins filled')
# Smooth the histogram
if smooth_sigma is not None:
filtered_histogram = gaussian_filter(
joint_histogram, sigma=smooth_sigma, order=0, mode='constant',
cval=0.0, truncate=4.0)
print(f'Smoothed histogram: {np.count_nonzero(filtered_histogram)}/'
f'{filtered_histogram.shape[0] * filtered_histogram.shape[1]} bins filled')
joint_histogram = filtered_histogram
return joint_histogram
def get_bond_length_arrays(atom_mapping):
bond_arrays = []
for i in range(3):
bond_dict = getattr(constants, f'bonds{i + 1}')
bond_array = np.zeros((len(atom_mapping), len(atom_mapping)))
for a1 in atom_mapping.keys():
for a2 in atom_mapping.keys():
if a1 in bond_dict and a2 in bond_dict[a1]:
bond_len = bond_dict[a1][a2]
else:
bond_len = 0
bond_array[atom_mapping[a1], atom_mapping[a2]] = bond_len
assert np.all(bond_array == bond_array.T)
bond_arrays.append(bond_array)
return bond_arrays
def get_lennard_jones_rm(atom_mapping):
# Bond radii for the Lennard-Jones potential
LJ_rm = np.zeros((len(atom_mapping), len(atom_mapping)))
for a1 in atom_mapping.keys():
for a2 in atom_mapping.keys():
all_bond_lengths = []
for btype in ['bonds1', 'bonds2', 'bonds3']:
bond_dict = getattr(constants, btype)
if a1 in bond_dict and a2 in bond_dict[a1]:
all_bond_lengths.append(bond_dict[a1][a2])
if len(all_bond_lengths) > 0:
# take the shortest possible bond length because slightly larger
# values aren't penalized as much
bond_len = min(all_bond_lengths)
else:
# Replace missing values with sum of average covalent radii
bond_len = covalent_radii[a1] + covalent_radii[a2]
LJ_rm[atom_mapping[a1], atom_mapping[a2]] = bond_len
assert np.all(LJ_rm == LJ_rm.T)
return LJ_rm
def get_type_histograms(lig_one_hot, pocket_one_hot, atom_encoder, aa_encoder):
atom_decoder = list(atom_encoder.keys())
atom_counts = {k: 0 for k in atom_encoder.keys()}
for a in [atom_decoder[x] for x in lig_one_hot.argmax(1)]:
atom_counts[a] += 1
aa_decoder = list(aa_encoder.keys())
aa_counts = {k: 0 for k in aa_encoder.keys()}
for r in [aa_decoder[x] for x in pocket_one_hot.argmax(1)]:
aa_counts[r] += 1
return atom_counts, aa_counts
def saveall(filename, pdb_and_mol_ids, lig_coords, lig_one_hot, lig_mask,
pocket_coords, pocket_quaternion, pocket_one_hot, pocket_mask):
np.savez(filename,
names=pdb_and_mol_ids,
lig_coords=lig_coords,
lig_one_hot=lig_one_hot,
lig_mask=lig_mask,
pocket_coords=pocket_coords,
pocket_quaternion=pocket_quaternion,
pocket_one_hot=pocket_one_hot,
pocket_mask=pocket_mask
)
return True
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('basedir', type=Path)
parser.add_argument('--outdir', type=Path, default=None)
parser.add_argument('--qed_thresh', type=float, default=0.3)
parser.add_argument('--max_occurences', type=int, default=50)
parser.add_argument('--num_val', type=int, default=300)
parser.add_argument('--num_test', type=int, default=300)
parser.add_argument('--dist_cutoff', type=float, default=8.0)
parser.add_argument('--ca_only', action='store_true')
parser.add_argument('--random_seed', type=int, default=42)
parser.add_argument('--make_split', action='store_true')
args = parser.parse_args()
pdbdir = args.basedir / 'BindingMOAD_2020/'
# Make output directory
if args.outdir is None:
suffix = '' if 'H' in atom_dict else '_noH'
suffix += '_ca_only' if args.ca_only else '_full'
processed_dir = Path(args.basedir, f'processed{suffix}')
else:
processed_dir = args.outdir
processed_dir.mkdir(exist_ok=True, parents=True)
if args.make_split:
# Process the label file
csv_path = args.basedir / 'every.csv'
ligand_dict = read_label_file(csv_path)
ligand_dict = compute_druglikeness(ligand_dict)
filtered_examples = filter_and_flatten(
ligand_dict, args.qed_thresh, args.max_occurences, args.random_seed)
print(f'{len(filtered_examples)} examples after filtering')
# Make data split
data_split = split_by_ec_number(filtered_examples, args.num_val,
args.num_test)
else:
# Use precomputed data split
data_split = {}
for split in ['test', 'val', 'train']:
with open(f'data/moad_{split}.txt', 'r') as f:
pocket_ids = f.read().split(',')
# (ec-number, protein, molecule tuple)
data_split[split] = [(None, x.split('_')[0][:4], (x.split('_')[1],))
for x in pocket_ids]
n_train_before = len(data_split['train'])
n_val_before = len(data_split['val'])
n_test_before = len(data_split['test'])
# Read and process PDB files
n_samples_after = {}
for split in data_split.keys():
lig_coords = []
lig_one_hot = []
lig_mask = []
pocket_coords = []
pocket_one_hot = []
pocket_mask = []
pdb_and_mol_ids = []
receptors = []
count = 0
pdb_sdf_dir = processed_dir / split
pdb_sdf_dir.mkdir(exist_ok=True)
n_tot = len(data_split[split])
pair_dict = ligand_list_to_dict(data_split[split])
tic = time()
num_failed = 0
with tqdm(total=n_tot) as pbar:
for p in pair_dict:
pdb_successful = set()
# try all available .bio files
for pdbfile in sorted(pdbdir.glob(f"{p.lower()}.bio*")):
# Skip if all ligands have been processed already
if len(pair_dict[p]) == len(pdb_successful):
continue
pdb_struct = PDBParser(QUIET=True).get_structure('', pdbfile)
struct_copy = pdb_struct.copy()
n_bio_successful = 0
for m in pair_dict[p]:
# Skip already processed ligand
if m[0] in pdb_successful:
continue
ligand_name, ligand_chain, ligand_resi = m[0].split(':')
ligand_resi = int(ligand_resi)
try:
ligand_data, pocket_data = process_ligand_and_pocket(
pdb_struct, ligand_name, ligand_chain, ligand_resi,
dist_cutoff=args.dist_cutoff, ca_only=args.ca_only)
except (KeyError, AssertionError, FileNotFoundError,
IndexError, ValueError) as e:
# print(type(e).__name__, e)
continue
pdb_and_mol_ids.append(f"{p}_{m[0]}")
receptors.append(pdbfile.name)
lig_coords.append(ligand_data['lig_coords'])
lig_one_hot.append(ligand_data['lig_one_hot'])
lig_mask.append(
count * np.ones(len(ligand_data['lig_coords'])))
pocket_coords.append(pocket_data['pocket_coords'])
# pocket_quaternion.append(
# pocket_data['pocket_quaternion'])
pocket_one_hot.append(pocket_data['pocket_one_hot'])
pocket_mask.append(
count * np.ones(len(pocket_data['pocket_coords'])))
count += 1
pdb_successful.add(m[0])
n_bio_successful += 1
# Save additional files for affinity analysis
if split in {'val', 'test'}:
# if split in {'val', 'test', 'train'}:
# remove ligand from receptor
try:
struct_copy[0][ligand_chain].detach_child((f'H_{ligand_name}', ligand_resi, ' '))
except KeyError:
warnings.warn(f"Could not find ligand {(f'H_{ligand_name}', ligand_resi, ' ')} in {pdbfile}")
continue
# Create SDF file
atom_types = [atom_decoder[np.argmax(i)] for i in ligand_data['lig_one_hot']]
xyz_file = Path(pdb_sdf_dir, 'tmp.xyz')
utils.write_xyz_file(ligand_data['lig_coords'], atom_types, xyz_file)
obConversion = openbabel.OBConversion()
obConversion.SetInAndOutFormats("xyz", "sdf")
mol = openbabel.OBMol()
obConversion.ReadFile(mol, str(xyz_file))
xyz_file.unlink()
name = f"{p}-{pdbfile.suffix[1:]}_{m[0]}"
sdf_file = Path(pdb_sdf_dir, f'{name}.sdf')
obConversion.WriteFile(mol, str(sdf_file))
# specify pocket residues
with open(Path(pdb_sdf_dir, f'{name}.txt'), 'w') as f:
f.write(' '.join(pocket_data['pocket_ids']))
if split in {'val', 'test'} and n_bio_successful > 0:
# if split in {'val', 'test', 'train'} and n_bio_successful > 0:
# create receptor PDB file
pdb_file_out = Path(pdb_sdf_dir, f'{p}-{pdbfile.suffix[1:]}.pdb')
io = PDBIO()
io.set_structure(struct_copy)
io.save(str(pdb_file_out), select=Model0())
pbar.update(len(pair_dict[p]))
num_failed += (len(pair_dict[p]) - len(pdb_successful))
pbar.set_description(f'#failed: {num_failed}')
lig_coords = np.concatenate(lig_coords, axis=0)
lig_one_hot = np.concatenate(lig_one_hot, axis=0)
lig_mask = np.concatenate(lig_mask, axis=0)
pocket_coords = np.concatenate(pocket_coords, axis=0)
pocket_one_hot = np.concatenate(pocket_one_hot, axis=0)
pocket_mask = np.concatenate(pocket_mask, axis=0)
np.savez(processed_dir / f'{split}.npz', names=pdb_and_mol_ids,
receptors=receptors, lig_coords=lig_coords,
lig_one_hot=lig_one_hot, lig_mask=lig_mask,
pocket_coords=pocket_coords, pocket_one_hot=pocket_one_hot,
pocket_mask=pocket_mask)
n_samples_after[split] = len(pdb_and_mol_ids)
print(f"Processing {split} set took {(time() - tic)/60.0:.2f} minutes")
# --------------------------------------------------------------------------
# Compute statistics & additional information
# --------------------------------------------------------------------------
with np.load(processed_dir / 'train.npz', allow_pickle=True) as data:
lig_mask = data['lig_mask']
pocket_mask = data['pocket_mask']
lig_coords = data['lig_coords']
lig_one_hot = data['lig_one_hot']
pocket_one_hot = data['pocket_one_hot']
# Compute SMILES for all training examples
train_smiles = compute_smiles(lig_coords, lig_one_hot, lig_mask)
np.save(processed_dir / 'train_smiles.npy', train_smiles)
# Joint histogram of number of ligand and pocket nodes
n_nodes = get_n_nodes(lig_mask, pocket_mask, smooth_sigma=1.0)
np.save(Path(processed_dir, 'size_distribution.npy'), n_nodes)
# Convert bond length dictionaries to arrays for batch processing
bonds1, bonds2, bonds3 = get_bond_length_arrays(atom_dict)
# Get bond length definitions for Lennard-Jones potential
rm_LJ = get_lennard_jones_rm(atom_dict)
# Get histograms of ligand and pocket node types
atom_hist, aa_hist = get_type_histograms(lig_one_hot, pocket_one_hot,
atom_dict, amino_acid_dict)
# Create summary string
summary_string = '# SUMMARY\n\n'
summary_string += '# Before processing\n'
summary_string += f'num_samples train: {n_train_before}\n'
summary_string += f'num_samples val: {n_val_before}\n'
summary_string += f'num_samples test: {n_test_before}\n\n'
summary_string += '# After processing\n'
summary_string += f"num_samples train: {n_samples_after['train']}\n"
summary_string += f"num_samples val: {n_samples_after['val']}\n"
summary_string += f"num_samples test: {n_samples_after['test']}\n\n"
summary_string += '# Info\n'
summary_string += f"'atom_encoder': {atom_dict}\n"
summary_string += f"'atom_decoder': {list(atom_dict.keys())}\n"
summary_string += f"'aa_encoder': {amino_acid_dict}\n"
summary_string += f"'aa_decoder': {list(amino_acid_dict.keys())}\n"
summary_string += f"'bonds1': {bonds1.tolist()}\n"
summary_string += f"'bonds2': {bonds2.tolist()}\n"
summary_string += f"'bonds3': {bonds3.tolist()}\n"
summary_string += f"'lennard_jones_rm': {rm_LJ.tolist()}\n"
summary_string += f"'atom_hist': {atom_hist}\n"
summary_string += f"'aa_hist': {aa_hist}\n"
summary_string += f"'n_nodes': {n_nodes.tolist()}\n"
# Write summary to text file
with open(processed_dir / 'summary.txt', 'w') as f:
f.write(summary_string)
# Print summary
print(summary_string)