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# small script to extract the ligand and save it in a separate file because GNINA will use the ligand position as initial pose
import os
import plotly.express as px
import time
from argparse import FileType, ArgumentParser
import numpy as np
import pandas as pd
import wandb
from biopandas.pdb import PandasPdb
from rdkit import Chem
from tqdm import tqdm
from datasets.pdbbind import read_mol
from datasets.process_mols import read_molecule
from utils.utils import read_strings_from_txt, get_symmetry_rmsd
parser = ArgumentParser()
parser.add_argument('--config', type=FileType(mode='r'), default=None)
parser.add_argument('--run_name', type=str, default='gnina_results', help='')
parser.add_argument('--data_dir', type=str, default='data/PDBBind_processed', help='')
parser.add_argument('--results_path', type=str, default='results/user_inference', help='Path to folder with trained model and hyperparameters')
parser.add_argument('--file_suffix', type=str, default='_baseline_ligand.pdb', help='Path to folder with trained model and hyperparameters')
parser.add_argument('--project', type=str, default='ligbind_inf', help='')
parser.add_argument('--wandb', action='store_true', default=False, help='')
parser.add_argument('--file_to_exclude', type=str, default=None, help='')
parser.add_argument('--all_dirs_in_results', action='store_true', default=True, help='Evaluate all directories in the results path instead of using directly looking for the names')
parser.add_argument('--num_predictions', type=int, default=10, help='')
parser.add_argument('--no_id_in_filename', action='store_true', default=False, help='')
args = parser.parse_args()
print('Reading paths and names.')
names = read_strings_from_txt(f'data/splits/timesplit_test')
names_no_rec_overlap = read_strings_from_txt(f'data/splits/timesplit_test_no_rec_overlap')
results_path_containments = os.listdir(args.results_path)
if args.wandb:
wandb.init(
entity='coarse-graining-mit',
settings=wandb.Settings(start_method="fork"),
project=args.project,
name=args.run_name,
config=args
)
all_times = []
successful_names_list = []
rmsds_list = []
centroid_distances_list = []
min_cross_distances_list = []
min_self_distances_list = []
without_rec_overlap_list = []
start_time = time.time()
for i, name in enumerate(tqdm(names)):
mol = read_mol(args.data_dir, name, remove_hs=True)
mol = Chem.RemoveAllHs(mol)
orig_ligand_pos = np.array(mol.GetConformer().GetPositions())
if args.all_dirs_in_results:
directory_with_name = [directory for directory in results_path_containments if name in directory][0]
ligand_pos = []
for i in range(args.num_predictions):
file_paths = os.listdir(os.path.join(args.results_path, directory_with_name))
file_path = [path for path in file_paths if f'rank{i+1}' in path][0]
if args.file_to_exclude is not None and args.file_to_exclude in file_path: continue
mol_pred = read_molecule(os.path.join(args.results_path, directory_with_name, file_path),remove_hs=True, sanitize=True)
mol_pred = Chem.RemoveAllHs(mol_pred)
ligand_pos.append(mol_pred.GetConformer().GetPositions())
ligand_pos = np.asarray(ligand_pos)
else:
if not os.path.exists(os.path.join(args.results_path, name, f'{"" if args.no_id_in_filename else name}{args.file_suffix}')): raise Exception('path did not exists:', os.path.join(args.results_path, name, f'{"" if args.no_id_in_filename else name}{args.file_suffix}'))
mol_pred = read_molecule(os.path.join(args.results_path, name, f'{"" if args.no_id_in_filename else name}{args.file_suffix}'), remove_hs=True, sanitize=True)
if mol_pred == None:
print("Skipping ", name, ' because RDKIT could not read it.')
continue
mol_pred = Chem.RemoveAllHs(mol_pred)
ligand_pos = np.asarray([np.array(mol_pred.GetConformer(i).GetPositions()) for i in range(args.num_predictions)])
try:
rmsd = get_symmetry_rmsd(mol, orig_ligand_pos, [l for l in ligand_pos], mol_pred)
except Exception as e:
print("Using non corrected RMSD because of the error:", e)
rmsd = np.sqrt(((ligand_pos - orig_ligand_pos) ** 2).sum(axis=2).mean(axis=1))
rmsds_list.append(rmsd)
centroid_distances_list.append(np.linalg.norm(ligand_pos.mean(axis=1) - orig_ligand_pos[None,:].mean(axis=1), axis=1))
rec_path = os.path.join(args.data_dir, name, f'{name}_protein_processed.pdb')
if not os.path.exists(rec_path):
rec_path = os.path.join(args.data_dir, name,f'{name}_protein_obabel_reduce.pdb')
rec = PandasPdb().read_pdb(rec_path)
rec_df = rec.df['ATOM']
receptor_pos = rec_df[['x_coord', 'y_coord', 'z_coord']].to_numpy().squeeze().astype(np.float32)
receptor_pos = np.tile(receptor_pos, (args.num_predictions, 1, 1))
cross_distances = np.linalg.norm(receptor_pos[:, :, None, :] - ligand_pos[:, None, :, :], axis=-1)
self_distances = np.linalg.norm(ligand_pos[:, :, None, :] - ligand_pos[:, None, :, :], axis=-1)
self_distances = np.where(np.eye(self_distances.shape[2]), np.inf, self_distances)
min_cross_distances_list.append(np.min(cross_distances, axis=(1,2)))
min_self_distances_list.append(np.min(self_distances, axis=(1, 2)))
successful_names_list.append(name)
without_rec_overlap_list.append(1 if name in names_no_rec_overlap else 0)
performance_metrics = {}
for overlap in ['', 'no_overlap_']:
if 'no_overlap_' == overlap:
without_rec_overlap = np.array(without_rec_overlap_list, dtype=bool)
rmsds = np.array(rmsds_list)[without_rec_overlap]
centroid_distances = np.array(centroid_distances_list)[without_rec_overlap]
min_cross_distances = np.array(min_cross_distances_list)[without_rec_overlap]
min_self_distances = np.array(min_self_distances_list)[without_rec_overlap]
successful_names = np.array(successful_names_list)[without_rec_overlap]
else:
rmsds = np.array(rmsds_list)
centroid_distances = np.array(centroid_distances_list)
min_cross_distances = np.array(min_cross_distances_list)
min_self_distances = np.array(min_self_distances_list)
successful_names = np.array(successful_names_list)
np.save(os.path.join(args.results_path, f'{overlap}rmsds.npy'), rmsds)
np.save(os.path.join(args.results_path, f'{overlap}names.npy'), successful_names)
np.save(os.path.join(args.results_path, f'{overlap}min_cross_distances.npy'), np.array(min_cross_distances))
np.save(os.path.join(args.results_path, f'{overlap}min_self_distances.npy'), np.array(min_self_distances))
performance_metrics.update({
f'{overlap}steric_clash_fraction': (100 * (min_cross_distances < 0.4).sum() / len(min_cross_distances) / args.num_predictions).__round__(2),
f'{overlap}self_intersect_fraction': (100 * (min_self_distances < 0.4).sum() / len(min_self_distances) / args.num_predictions).__round__(2),
f'{overlap}mean_rmsd': rmsds[:,0].mean(),
f'{overlap}rmsds_below_2': (100 * (rmsds[:,0] < 2).sum() / len(rmsds[:,0])),
f'{overlap}rmsds_below_5': (100 * (rmsds[:,0] < 5).sum() / len(rmsds[:,0])),
f'{overlap}rmsds_percentile_25': np.percentile(rmsds[:,0], 25).round(2),
f'{overlap}rmsds_percentile_50': np.percentile(rmsds[:,0], 50).round(2),
f'{overlap}rmsds_percentile_75': np.percentile(rmsds[:,0], 75).round(2),
f'{overlap}mean_centroid': centroid_distances[:,0].mean().__round__(2),
f'{overlap}centroid_below_2': (100 * (centroid_distances[:,0] < 2).sum() / len(centroid_distances[:,0])).__round__(2),
f'{overlap}centroid_below_5': (100 * (centroid_distances[:,0] < 5).sum() / len(centroid_distances[:,0])).__round__(2),
f'{overlap}centroid_percentile_25': np.percentile(centroid_distances[:,0], 25).round(2),
f'{overlap}centroid_percentile_50': np.percentile(centroid_distances[:,0], 50).round(2),
f'{overlap}centroid_percentile_75': np.percentile(centroid_distances[:,0], 75).round(2),
})
top5_rmsds = np.min(rmsds[:, :5], axis=1)
top5_centroid_distances = centroid_distances[np.arange(rmsds.shape[0])[:,None],np.argsort(rmsds[:, :5], axis=1)][:,0]
top5_min_cross_distances = min_cross_distances[np.arange(rmsds.shape[0])[:,None],np.argsort(rmsds[:, :5], axis=1)][:,0]
top5_min_self_distances = min_self_distances[np.arange(rmsds.shape[0])[:,None],np.argsort(rmsds[:, :5], axis=1)][:,0]
performance_metrics.update({
f'{overlap}top5_steric_clash_fraction': (100 * (top5_min_cross_distances < 0.4).sum() / len(top5_min_cross_distances)).__round__(2),
f'{overlap}top5_self_intersect_fraction': (100 * (top5_min_self_distances < 0.4).sum() / len(top5_min_self_distances)).__round__(2),
f'{overlap}top5_rmsds_below_2': (100 * (top5_rmsds < 2).sum() / len(top5_rmsds)).__round__(2),
f'{overlap}top5_rmsds_below_5': (100 * (top5_rmsds < 5).sum() / len(top5_rmsds)).__round__(2),
f'{overlap}top5_rmsds_percentile_25': np.percentile(top5_rmsds, 25).round(2),
f'{overlap}top5_rmsds_percentile_50': np.percentile(top5_rmsds, 50).round(2),
f'{overlap}top5_rmsds_percentile_75': np.percentile(top5_rmsds, 75).round(2),
f'{overlap}top5_centroid_below_2': (100 * (top5_centroid_distances < 2).sum() / len(top5_centroid_distances)).__round__(2),
f'{overlap}top5_centroid_below_5': (100 * (top5_centroid_distances < 5).sum() / len(top5_centroid_distances)).__round__(2),
f'{overlap}top5_centroid_percentile_25': np.percentile(top5_centroid_distances, 25).round(2),
f'{overlap}top5_centroid_percentile_50': np.percentile(top5_centroid_distances, 50).round(2),
f'{overlap}top5_centroid_percentile_75': np.percentile(top5_centroid_distances, 75).round(2),
})
top10_rmsds = np.min(rmsds[:, :10], axis=1)
top10_centroid_distances = centroid_distances[np.arange(rmsds.shape[0])[:,None],np.argsort(rmsds[:, :10], axis=1)][:,0]
top10_min_cross_distances = min_cross_distances[np.arange(rmsds.shape[0])[:,None],np.argsort(rmsds[:, :10], axis=1)][:,0]
top10_min_self_distances = min_self_distances[np.arange(rmsds.shape[0])[:,None],np.argsort(rmsds[:, :10], axis=1)][:,0]
performance_metrics.update({
f'{overlap}top10_self_intersect_fraction': (100 * (top10_min_self_distances < 0.4).sum() / len(top10_min_self_distances)).__round__(2),
f'{overlap}top10_steric_clash_fraction': ( 100 * (top10_min_cross_distances < 0.4).sum() / len(top10_min_cross_distances)).__round__(2),
f'{overlap}top10_rmsds_below_2': (100 * (top10_rmsds < 2).sum() / len(top10_rmsds)).__round__(2),
f'{overlap}top10_rmsds_below_5': (100 * (top10_rmsds < 5).sum() / len(top10_rmsds)).__round__(2),
f'{overlap}top10_rmsds_percentile_25': np.percentile(top10_rmsds, 25).round(2),
f'{overlap}top10_rmsds_percentile_50': np.percentile(top10_rmsds, 50).round(2),
f'{overlap}top10_rmsds_percentile_75': np.percentile(top10_rmsds, 75).round(2),
f'{overlap}top10_centroid_below_2': (100 * (top10_centroid_distances < 2).sum() / len(top10_centroid_distances)).__round__(2),
f'{overlap}top10_centroid_below_5': (100 * (top10_centroid_distances < 5).sum() / len(top10_centroid_distances)).__round__(2),
f'{overlap}top10_centroid_percentile_25': np.percentile(top10_centroid_distances, 25).round(2),
f'{overlap}top10_centroid_percentile_50': np.percentile(top10_centroid_distances, 50).round(2),
f'{overlap}top10_centroid_percentile_75': np.percentile(top10_centroid_distances, 75).round(2),
})
for k in performance_metrics:
print(k, performance_metrics[k])
if args.wandb:
wandb.log(performance_metrics)
histogram_metrics_list = [('rmsd', rmsds[:,0]),
('centroid_distance', centroid_distances[:,0]),
('mean_rmsd', rmsds[:,0]),
('mean_centroid_distance', centroid_distances[:,0])]
histogram_metrics_list.append(('top5_rmsds', top5_rmsds))
histogram_metrics_list.append(('top5_centroid_distances', top5_centroid_distances))
histogram_metrics_list.append(('top10_rmsds', top10_rmsds))
histogram_metrics_list.append(('top10_centroid_distances', top10_centroid_distances))
os.makedirs(f'.plotly_cache/baseline_cache', exist_ok=True)
images = []
for metric_name, metric in histogram_metrics_list:
d = {args.results_path: metric}
df = pd.DataFrame(data=d)
fig = px.ecdf(df, width=900, height=600, range_x=[0, 40])
fig.add_vline(x=2, annotation_text='2 A;', annotation_font_size=20, annotation_position="top right",
line_dash='dash', line_color='firebrick', annotation_font_color='firebrick')
fig.add_vline(x=5, annotation_text='5 A;', annotation_font_size=20, annotation_position="top right",
line_dash='dash', line_color='green', annotation_font_color='green')
fig.update_xaxes(title=f'{metric_name} in Angstrom', title_font={"size": 20}, tickfont={"size": 20})
fig.update_yaxes(title=f'Fraction of predictions with lower error', title_font={"size": 20},
tickfont={"size": 20})
fig.update_layout(autosize=False, margin={'l': 0, 'r': 0, 't': 0, 'b': 0}, plot_bgcolor='white',
paper_bgcolor='white', legend_title_text='Method', legend_title_font_size=17,
legend=dict(yanchor="bottom", y=0.1, xanchor="right", x=0.99, font=dict(size=17), ), )
fig.update_xaxes(showgrid=True, gridcolor='lightgrey')
fig.update_yaxes(showgrid=True, gridcolor='lightgrey')
fig.write_image(os.path.join(f'.plotly_cache/baseline_cache', f'{metric_name}.png'))
wandb.log({metric_name: wandb.Image(os.path.join(f'.plotly_cache/baseline_cache', f'{metric_name}.png'), caption=f"{metric_name}")})
images.append(wandb.Image(os.path.join(f'.plotly_cache/baseline_cache', f'{metric_name}.png'), caption=f"{metric_name}"))
wandb.log({'images': images}) |