Revert "restored whl files, deleted datasets and commented out example section"

This reverts commit aaec3835c66ba4545ba26ae0f6a2a779ae3ed8a2.

app.py

@@ -533,74 +533,74 @@ with demo:
     out = gr.Dropdown(interactive=True, label="Ranked samples")
     mol = gr.HTML()
     output_file = gr.File(file_count="single", label="Output files")
-    # gr.Examples(
-    #     [
-    #         [
-    #             "6w70",
-    #             "examples/6w70.pdb",
-    #             "COc1ccc(cc1)n2c3c(c(n2)C(=O)N)CCN(C3=O)c4ccc(cc4)N5CCCCC5=O",
-    #             "examples/6w70_ligand.sdf",
-    #             20,
-    #             10,
-    #             18,
-    #             True
-    #         ],
-    #         [
-    #             "6moa",
-    #             "examples/6moa_protein_processed.pdb",
-    #             "",
-    #             "examples/6moa_ligand.sdf",
-    #             20,
-    #             10,
-    #             18,
-    #             True
-    #         ],
-    #         [
-    #             "",
-    #             "examples/6o5u_protein_processed.pdb",
-    #             "",
-    #             "examples/6o5u_ligand.sdf",
-    #             20,
-    #             10,
-    #             18,
-    #             True
-    #         ],
-    #         [
-    #             "",
-    #             "examples/6o5u_protein_processed.pdb",
-    #             "[NH3+]C[C@H]1O[C@H](O[C@@H]2[C@@H]([NH3+])C[C@H]([C@@H]([C@H]2O)O[C@H]2O[C@H](CO)[C@H]([C@@H]([C@H]2O)[NH3+])O)[NH3+])[C@@H]([C@H]([C@@H]1O)O)O",
-    #             "examples/6o5u_ligand.sdf",
-    #             20,
-    #             10,
-    #             18,
-    #             True
-    #         ],
-    #         [
-    #             "",
-    #             "examples/6o5u_protein_processed.pdb",
-    #             "",
-    #             "examples/6o5u_ligand.sdf",
-    #             20,
-    #             10,
-    #             18,
-    #             True
-    #         ],
-    #         [
-    #             "",
-    #             "examples/6ahs_protein_processed.pdb",
-    #             "",
-    #             "examples/6ahs_ligand.sdf",
-    #             20,
-    #             10,
-    #             18,
-    #             True
-    #         ],
-    #     ],
-    #     [inp, file, ligand_inp, ligand_file, n_it, n_samples, actual_steps, no_final_step_noise],
-    #     [mol, out, filenames, pdb, output_file],
-    #     # fn=update,
-    #     # cache_examples=True,
-    # )
+    gr.Examples(
+        [
+            [
+                "6w70",
+                "examples/6w70.pdb",
+                "COc1ccc(cc1)n2c3c(c(n2)C(=O)N)CCN(C3=O)c4ccc(cc4)N5CCCCC5=O",
+                "examples/6w70_ligand.sdf",
+                20,
+                10,
+                18,
+                True
+            ],
+            [
+                "6moa",
+                "examples/6moa_protein_processed.pdb",
+                "",
+                "examples/6moa_ligand.sdf",
+                20,
+                10,
+                18,
+                True
+            ],
+            [
+                "",
+                "examples/6o5u_protein_processed.pdb",
+                "",
+                "examples/6o5u_ligand.sdf",
+                20,
+                10,
+                18,
+                True
+            ],
+            [
+                "",
+                "examples/6o5u_protein_processed.pdb",
+                "[NH3+]C[C@H]1O[C@H](O[C@@H]2[C@@H]([NH3+])C[C@H]([C@@H]([C@H]2O)O[C@H]2O[C@H](CO)[C@H]([C@@H]([C@H]2O)[NH3+])O)[NH3+])[C@@H]([C@H]([C@@H]1O)O)O",
+                "examples/6o5u_ligand.sdf",
+                20,
+                10,
+                18,
+                True
+            ],
+            [
+                "",
+                "examples/6o5u_protein_processed.pdb",
+                "",
+                "examples/6o5u_ligand.sdf",
+                20,
+                10,
+                18,
+                True
+            ],
+            [
+                "",
+                "examples/6ahs_protein_processed.pdb",
+                "",
+                "examples/6ahs_ligand.sdf",
+                20,
+                10,
+                18,
+                True
+            ],
+        ],
+        [inp, file, ligand_inp, ligand_file, n_it, n_samples, actual_steps, no_final_step_noise],
+        [mol, out, filenames, pdb, output_file],
+        # fn=update,
+        # cache_examples=True,
+    )
     btn.click(
         fn=update,
         inputs=[inp, file, ligand_inp, ligand_file, n_it, n_samples, actual_steps, no_final_step_noise],

baselines/baseline_evaluation.py

@@ -0,0 +1,219 @@
+# 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})

baselines/baseline_gnina.py

@@ -0,0 +1,175 @@
+# 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 shutil
+import subprocess
+import sys
+
+import time
+from argparse import ArgumentParser, FileType
+from datetime import datetime
+
+import numpy as np
+import pandas as pd
+from biopandas.pdb import PandasPdb
+from rdkit import Chem
+from rdkit.Chem import AllChem, MolToPDBFile
+from scipy.spatial.distance import cdist
+
+from datasets.pdbbind import read_mol
+from utils.utils import read_strings_from_txt
+
+parser = ArgumentParser()
+parser.add_argument('--data_dir', type=str, default='data/PDBBind_processed', help='')
+parser.add_argument('--file_suffix', type=str, default='_baseline_ligand', help='Path to folder with trained model and hyperparameters')
+parser.add_argument('--results_path', type=str, default='results/gnina_predictions', help='')
+parser.add_argument('--complex_names_path', type=str, default='data/splits/timesplit_test', help='')
+parser.add_argument('--seed_molecules_path', type=str, default=None, help='Use the molecules at seed molecule path as initialization and only search around them')
+parser.add_argument('--seed_molecule_filename', type=str, default='equibind_corrected.sdf', help='Use the molecules at seed molecule path as initialization and only search around them')
+parser.add_argument('--smina', action='store_true', default=False, help='')
+parser.add_argument('--no_gpu', action='store_true', default=False, help='')
+parser.add_argument('--exhaustiveness', type=int, default=8, help='')
+parser.add_argument('--num_cpu', type=int, default=16, help='')
+parser.add_argument('--pocket_mode', action='store_true', default=False, help='')
+parser.add_argument('--pocket_cutoff', type=int, default=5, help='')
+parser.add_argument('--num_modes', type=int, default=10, help='')
+parser.add_argument('--autobox_add', type=int, default=4, help='')
+parser.add_argument('--use_p2rank_pocket', action='store_true', default=False, help='')
+parser.add_argument('--skip_p2rank', action='store_true', default=False, help='')
+parser.add_argument('--prank_path', type=str, default='/Users/hstark/projects/p2rank_2.3/prank', help='')
+parser.add_argument('--skip_existing', action='store_true', default=False, help='')
+
+
+
+
+
+args = parser.parse_args()
+
+class Logger(object):
+    def __init__(self, logpath, syspart=sys.stdout):
+        self.terminal = syspart
+        self.log = open(logpath, "a")
+
+    def write(self, message):
+        self.terminal.write(message)
+        self.log.write(message)
+        self.log.flush()
+
+    def flush(self):
+        # this flush method is needed for python 3 compatibility.
+        # this handles the flush command by doing nothing.
+        # you might want to specify some extra behavior here.
+        pass
+
+
+def log(*args):
+    print(f'[{datetime.now()}]', *args)
+
+
+# parameters
+names = read_strings_from_txt(args.complex_names_path)
+
+if os.path.exists(args.results_path) and not args.skip_existing:
+    shutil.rmtree(args.results_path)
+os.makedirs(args.results_path, exist_ok=True)
+sys.stdout = Logger(logpath=f'{args.results_path}/gnina.log', syspart=sys.stdout)
+sys.stderr = Logger(logpath=f'{args.results_path}/error.log', syspart=sys.stderr)
+
+p2rank_cache_path = "results/.p2rank_cache"
+if args.use_p2rank_pocket and not args.skip_p2rank:
+    os.makedirs(p2rank_cache_path, exist_ok=True)
+    pdb_files_cache = os.path.join(p2rank_cache_path,'pdb_files')
+    os.makedirs(pdb_files_cache, exist_ok=True)
+    with open(f"{p2rank_cache_path}/pdb_list_p2rank.txt", "w") as out:
+        for name in names:
+            shutil.copy(os.path.join(args.data_dir, name, f'{name}_protein_processed.pdb'), f'{pdb_files_cache}/{name}_protein_processed.pdb')
+            out.write(os.path.join('pdb_files', f'{name}_protein_processed.pdb\n'))
+    cmd = f"bash {args.prank_path} predict {p2rank_cache_path}/pdb_list_p2rank.txt -o {p2rank_cache_path}/p2rank_output -threads 4"
+    os.system(cmd)
+
+
+all_times = []
+start_time = time.time()
+for i, name in enumerate(names):
+    os.makedirs(os.path.join(args.results_path, name), exist_ok=True)
+    log('\n')
+    log(f'complex {i} of {len(names)}')
+    # call gnina to find binding pose
+    rec_path = os.path.join(args.data_dir, name, f'{name}_protein_processed.pdb')
+    prediction_output_name = os.path.join(args.results_path, name, f'{name}{args.file_suffix}.pdb')
+    log_path = os.path.join(args.results_path, name, f'{name}{args.file_suffix}.log')
+    if args.seed_molecules_path is not None: seed_mol_path = os.path.join(args.seed_molecules_path, name, f'{args.seed_molecule_filename}')
+    if args.skip_existing and os.path.exists(prediction_output_name): continue
+
+    if args.pocket_mode:
+        mol = read_mol(args.data_dir, name, remove_hs=False)
+        rec = PandasPdb().read_pdb(rec_path)
+        rec_df = rec.get(s='c-alpha')
+        rec_pos = rec_df[['x_coord', 'y_coord', 'z_coord']].to_numpy().squeeze().astype(np.float32)
+        lig_pos = mol.GetConformer().GetPositions()
+        d = cdist(rec_pos, lig_pos)
+        label = np.any(d < args.pocket_cutoff, axis=1)
+
+        if np.any(label):
+            center_pocket = rec_pos[label].mean(axis=0)
+        else:
+            print("No pocket residue below minimum distance ", args.pocket_cutoff, "taking closest at", np.min(d))
+            center_pocket = rec_pos[np.argmin(np.min(d, axis=1)[0])]
+        radius_pocket = np.max(np.linalg.norm(lig_pos - center_pocket[None, :], axis=1))
+        diameter_pocket = radius_pocket * 2
+        center_x = center_pocket[0]
+        size_x = diameter_pocket + 8
+        center_y = center_pocket[1]
+        size_y = diameter_pocket + 8
+        center_z = center_pocket[2]
+        size_z = diameter_pocket + 8
+
+
+    mol_rdkit = read_mol(args.data_dir, name, remove_hs=False)
+    single_time = time.time()
+
+    mol_rdkit.RemoveAllConformers()
+    ps = AllChem.ETKDGv2()
+    id = AllChem.EmbedMolecule(mol_rdkit, ps)
+    if id == -1:
+        print('rdkit pos could not be generated without using random pos. using random pos now.')
+        ps.useRandomCoords = True
+        AllChem.EmbedMolecule(mol_rdkit, ps)
+        AllChem.MMFFOptimizeMolecule(mol_rdkit, confId=0)
+    rdkit_mol_path = os.path.join(args.data_dir, name, f'{name}_rdkit_ligand.pdb')
+    MolToPDBFile(mol_rdkit, rdkit_mol_path)
+
+    fallback_without_p2rank = False
+    if args.use_p2rank_pocket:
+        df = pd.read_csv(f'{p2rank_cache_path}/p2rank_output/{name}_protein_processed.pdb_predictions.csv')
+        rdkit_lig_pos = mol_rdkit.GetConformer().GetPositions()
+        diameter_pocket = np.max(cdist(rdkit_lig_pos, rdkit_lig_pos))
+        size_x = diameter_pocket + args.autobox_add * 2
+        size_y = diameter_pocket + args.autobox_add * 2
+        size_z = diameter_pocket + args.autobox_add * 2
+        if df.empty:
+            fallback_without_p2rank = True
+        else:
+            center_x = df.iloc[0]['   center_x']
+            center_y = df.iloc[0]['   center_y']
+            center_z = df.iloc[0]['   center_z']
+
+
+
+    log(f'processing {rec_path}')
+    if not args.pocket_mode and not args.use_p2rank_pocket or fallback_without_p2rank:
+        return_code = subprocess.run(
+            f"gnina --receptor {rec_path} --ligand {rdkit_mol_path} --num_modes {args.num_modes} -o {prediction_output_name} {'--no_gpu' if args.no_gpu else ''} --autobox_ligand {rec_path if args.seed_molecules_path is None else seed_mol_path} --autobox_add {args.autobox_add} --log {log_path} --exhaustiveness {args.exhaustiveness} --cpu {args.num_cpu} {'--cnn_scoring none' if args.smina else ''}",
+            shell=True)
+    else:
+        return_code = subprocess.run(
+            f"gnina --receptor {rec_path} --ligand {rdkit_mol_path} --num_modes {args.num_modes} -o {prediction_output_name} {'--no_gpu' if args.no_gpu else ''} --log {log_path} --exhaustiveness {args.exhaustiveness} --cpu {args.num_cpu} {'--cnn_scoring none' if args.smina else ''} --center_x {center_x} --center_y {center_y} --center_z {center_z} --size_x {size_x} --size_y {size_y} --size_z {size_z}",
+            shell=True)
+    log(return_code)
+    all_times.append(time.time() - single_time)
+
+    log("single time: --- %s seconds ---" % (time.time() - single_time))
+    log("time so far: --- %s seconds ---" % (time.time() - start_time))
+    log('\n')
+log(all_times)
+log("--- %s seconds ---" % (time.time() - start_time))

baselines/baseline_run_tankbind_parallel.sh

@@ -0,0 +1,5 @@
+for i in $(seq 0 15); do
+ 	python baseline_tankbind_runtime.py --parallel_id $i --parallel_tot 16 --prank_path /data/rsg/nlp/hstark/TankBind/packages/p2rank_2.3/prank --data_dir /data/rsg/nlp/hstark/ligbind/data/PDBBind_processed --split_path /data/rsg/nlp/hstark/ligbind/data/splits/timesplit_test --results_path /data/rsg/nlp/hstark/ligbind/results/tankbind_16_worker_runtime --device cpu --skip_p2rank --num_workers 1 --skip_multiple_pocket_outputs &
+done
+wait
+

baselines/baseline_tankbind_evaluation.py

@@ -0,0 +1,239 @@
+
+import copy
+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 rdkit.Chem import RemoveHs
+
+from tqdm import tqdm
+
+from datasets.pdbbind import read_mol
+from datasets.process_mols import read_molecule, read_sdf_or_mol2
+from utils.utils import read_strings_from_txt, get_symmetry_rmsd, remove_all_hs
+
+parser = ArgumentParser()
+parser.add_argument('--config', type=FileType(mode='r'), default=None)
+parser.add_argument('--run_name', type=str, default='tankbind', help='')
+parser.add_argument('--data_dir', type=str, default='data/PDBBind_processed', help='')
+parser.add_argument('--renumbered_atoms_dir', type=str, default='../TankBind/examples/tankbind_pdb/renumber_atom_index_same_as_smiles', help='')
+parser.add_argument('--results_path', type=str, default='results/tankbind_top5', 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=True, help='')
+parser.add_argument('--num_predictions', type=int, default=5, help='')
+args = parser.parse_args()
+
+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')
+
+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 = []
+rmsds_list = []
+unsym_rmsds_list = []
+centroid_distances_list = []
+min_cross_distances_list = []
+min_self_distances_list = []
+made_prediction_list = []
+steric_clash_list = []
+without_rec_overlap_list = []
+
+start_time = time.time()
+successful_names_list = []
+for i, name in enumerate(tqdm(names)):
+    mol, _ = read_sdf_or_mol2(f"{args.renumbered_atoms_dir}/{name}.sdf", None)
+    sm = Chem.MolToSmiles(mol)
+    m_order = list(mol.GetPropsAsDict(includePrivate=True, includeComputed=True)['_smilesAtomOutputOrder'])
+    mol = Chem.RenumberAtoms(mol, m_order)
+    mol = Chem.RemoveHs(mol)
+    orig_ligand_pos = np.array(mol.GetConformer().GetPositions())
+
+    assert(os.path.exists(os.path.join(args.results_path, name, f'{name}_tankbind_0.sdf')))
+    ligand_pos = []
+    for i in range(args.num_predictions):
+        if not os.path.exists(os.path.join(args.results_path, name, f'{name}_tankbind_{i}.sdf')): break
+        mol_pred, _ = read_sdf_or_mol2(os.path.join(args.results_path, name, f'{name}_tankbind_{i}.sdf'),None)
+        sm = Chem.MolToSmiles(mol_pred)
+        m_order = list(mol_pred.GetPropsAsDict(includePrivate=True, includeComputed=True)['_smilesAtomOutputOrder'])
+        mol_pred = Chem.RenumberAtoms(mol_pred, m_order)
+        mol_pred = RemoveHs(mol_pred)
+        ligand_pos.append(np.array(mol_pred.GetConformer().GetPositions()))
+    ligand_pos = np.asarray(ligand_pos)
+
+    try:
+        unsym_rmsd = np.sqrt(((ligand_pos - orig_ligand_pos) ** 2).sum(axis=2).mean(axis=1))
+        rmsd = np.array(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))
+
+    num_pockets = len(ligand_pos)
+    unsym_rmsds_list.append(np.lib.pad(unsym_rmsd, (0,10-len(unsym_rmsd)), 'constant', constant_values=(0)) )
+    rmsds_list.append(np.lib.pad(rmsd, (0,10-len(rmsd)), 'constant', constant_values=(0)) )
+    centroid_distance = np.linalg.norm(ligand_pos.mean(axis=1) - orig_ligand_pos[None,:].mean(axis=1), axis=1)
+    centroid_distances_list.append(np.lib.pad(centroid_distance, (0,10-len(rmsd)), 'constant', constant_values=(0)) )
+
+    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, (10, 1, 1))
+
+    ligand_pos_padded = np.lib.pad(ligand_pos, ((0,10-len(ligand_pos)), (0,0), (0,0)), 'constant', constant_values=(np.inf))
+    ligand_pos_padded_zero = np.lib.pad(ligand_pos, ((0, 10 - len(ligand_pos)), (0, 0), (0, 0)), 'constant',constant_values=0)
+    cross_distances = np.linalg.norm(receptor_pos[:, :, None, :] - ligand_pos_padded[:, None, :, :], axis=-1)
+    self_distances = np.linalg.norm(ligand_pos_padded_zero[:, :, None, :] - ligand_pos_padded_zero[:, None, :, :], axis=-1)
+    self_distances =  np.where(np.eye(self_distances.shape[2]), np.inf, self_distances)
+    min_self_distances_list.append(np.min(self_distances, axis=(1, 2)))
+    min_cross_distance = np.min(cross_distances, axis=(1, 2))
+    individual_made_prediction = np.lib.pad(np.ones(num_pockets), (0,10-len(rmsd)), 'constant', constant_values=(0))
+    made_prediction_list.append(individual_made_prediction)
+    min_cross_distances_list.append(min_cross_distance)
+    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)
+        unsym_rmsds = np.array(unsym_rmsds_list)[without_rec_overlap]
+        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]
+        made_prediction = np.array(made_prediction_list)[without_rec_overlap]
+        successful_names = np.array(successful_names_list)[without_rec_overlap]
+    else:
+        unsym_rmsds = np.array(unsym_rmsds_list)
+        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)
+        made_prediction = np.array(made_prediction_list)
+        successful_names = np.array(successful_names_list)
+
+    inf_rmsds = copy.deepcopy(rmsds)
+    inf_rmsds[~made_prediction.astype(bool)] = np.inf
+    inf_centroid_distances = copy.deepcopy(centroid_distances)
+    inf_centroid_distances[~made_prediction.astype(bool)] = np.inf
+
+    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'), np.array(successful_names))
+    np.save(os.path.join(args.results_path, f'{overlap}centroid_distances.npy'), centroid_distances)
+    np.save(os.path.join(args.results_path, f'{overlap}min_cross_distances.npy'), min_cross_distances)
+    np.save(os.path.join(args.results_path, f'{overlap}min_self_distances.npy'), min_self_distances)
+
+    performance_metrics.update({
+        f'{overlap}self_intersect_fraction': (100 * (min_self_distances[:, 0] < 0.4).sum() / len(min_self_distances[:, 0])),
+        f'{overlap}steric_clash_fraction': (100 * (min_cross_distances[:,0] < 0.4).sum() / len(min_cross_distances[:,0])),
+        f'{overlap}mean_rmsd': rmsds[:,0].mean(),
+        f'{overlap}unsym_rmsds_below_2': (100 * (unsym_rmsds[:,0] < 2).sum() / len(unsym_rmsds[:,0])),
+        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(inf_rmsds[:, :5], axis=1)
+    top5_centroid_distances = centroid_distances[np.arange(rmsds.shape[0])[:,None],np.argsort(inf_rmsds[:, :5], axis=1)][:,0]
+    top5_min_cross_distances = min_cross_distances[np.arange(rmsds.shape[0])[:,None],np.argsort(inf_rmsds[:, :5], axis=1)][:,0]
+    top5_min_self_distances = min_self_distances[np.arange(rmsds.shape[0])[:,None],np.argsort(inf_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(inf_rmsds[:, :10], axis=1)
+    top10_centroid_distances = centroid_distances[np.arange(rmsds.shape[0])[:,None],np.argsort(inf_rmsds[:, :10], axis=1)][:,0]
+    top10_min_cross_distances = min_cross_distances[np.arange(rmsds.shape[0])[:,None],np.argsort(inf_rmsds[:, :10], axis=1)][:,0]
+    top10_min_self_distances = min_self_distances[np.arange(rmsds.shape[0])[:,None],np.argsort(inf_rmsds[:, :10], axis=1)][:,0]
+    performance_metrics.update({
+        f'{overlap}top10_steric_clash_fraction': (100 * (top10_min_cross_distances < 0.4).sum() / len(top10_min_cross_distances)).__round__(2),
+        f'{overlap}top10_self_intersect_fraction': (100 * (top10_min_self_distances < 0.4).sum() / len(top10_min_self_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})

baselines/baseline_tankbind_runtime.py

@@ -0,0 +1,342 @@
+# This file needs to be ran in the TANKBind repository together with baseline_run_tankbind_parallel.sh
+
+import sys
+import time
+from multiprocessing import Pool
+
+
+import copy
+import warnings
+from argparse import ArgumentParser
+
+from rdkit.Chem import AllChem, RemoveHs
+
+from feature_utils import save_cleaned_protein, read_mol
+from generation_utils import get_LAS_distance_constraint_mask, get_info_pred_distance, write_with_new_coords
+import logging
+from torch_geometric.loader import DataLoader
+from tqdm import tqdm  # pip install tqdm if fails.
+from model import get_model
+# from utils import *
+import torch
+
+
+from data import TankBind_prediction
+
+import os
+import numpy as np
+import pandas as pd
+import rdkit.Chem as Chem
+from feature_utils import generate_sdf_from_smiles_using_rdkit
+from feature_utils import get_protein_feature
+from Bio.PDB import PDBParser
+from feature_utils import extract_torchdrug_feature_from_mol
+
+
+def read_strings_from_txt(path):
+    # every line will be one element of the returned list
+    with open(path) as file:
+        lines = file.readlines()
+        return [line.rstrip() for line in lines]
+
+
+def read_molecule(molecule_file, sanitize=False, calc_charges=False, remove_hs=False):
+    if molecule_file.endswith('.mol2'):
+        mol = Chem.MolFromMol2File(molecule_file, sanitize=False, removeHs=False)
+    elif molecule_file.endswith('.sdf'):
+        supplier = Chem.SDMolSupplier(molecule_file, sanitize=False, removeHs=False)
+        mol = supplier[0]
+    elif molecule_file.endswith('.pdbqt'):
+        with open(molecule_file) as file:
+            pdbqt_data = file.readlines()
+        pdb_block = ''
+        for line in pdbqt_data:
+            pdb_block += '{}\n'.format(line[:66])
+        mol = Chem.MolFromPDBBlock(pdb_block, sanitize=False, removeHs=False)
+    elif molecule_file.endswith('.pdb'):
+        mol = Chem.MolFromPDBFile(molecule_file, sanitize=False, removeHs=False)
+    else:
+        return ValueError('Expect the format of the molecule_file to be '
+                          'one of .mol2, .sdf, .pdbqt and .pdb, got {}'.format(molecule_file))
+    try:
+        if sanitize or calc_charges:
+            Chem.SanitizeMol(mol)
+
+        if calc_charges:
+            # Compute Gasteiger charges on the molecule.
+            try:
+                AllChem.ComputeGasteigerCharges(mol)
+            except:
+                warnings.warn('Unable to compute charges for the molecule.')
+
+        if remove_hs:
+            mol = Chem.RemoveHs(mol, sanitize=sanitize)
+    except:
+        return None
+
+    return mol
+
+
+def parallel_save_prediction(arguments):
+    dataset, y_pred_list, chosen,rdkit_mol_path, result_folder, name = arguments
+    for idx, line in chosen.iterrows():
+        pocket_name = line['pocket_name']
+        compound_name = line['compound_name']
+        ligandName = compound_name.split("_")[1]
+        dataset_index = line['dataset_index']
+        coords = dataset[dataset_index].coords.to('cpu')
+        protein_nodes_xyz = dataset[dataset_index].node_xyz.to('cpu')
+        n_compound = coords.shape[0]
+        n_protein = protein_nodes_xyz.shape[0]
+        y_pred = y_pred_list[dataset_index].reshape(n_protein, n_compound).to('cpu')
+        compound_pair_dis_constraint = torch.cdist(coords, coords)
+        mol = Chem.MolFromMolFile(rdkit_mol_path)
+        LAS_distance_constraint_mask = get_LAS_distance_constraint_mask(mol).bool()
+        pred_dist_info = get_info_pred_distance(coords, y_pred, protein_nodes_xyz, compound_pair_dis_constraint,
+                                                LAS_distance_constraint_mask=LAS_distance_constraint_mask,
+                                                n_repeat=1, show_progress=False)
+
+        toFile = f'{result_folder}/{name}_tankbind_chosen.sdf'
+        new_coords = pred_dist_info.sort_values("loss")['coords'].iloc[0].astype(np.double)
+        write_with_new_coords(mol, new_coords, toFile)
+
+if __name__ == '__main__':
+    tankbind_src_folder = "../tankbind"
+    sys.path.insert(0, tankbind_src_folder)
+    torch.set_num_threads(16)
+    parser = ArgumentParser()
+    parser.add_argument('--data_dir', type=str, default='/Users/hstark/projects/ligbind/data/PDBBind_processed', help='')
+    parser.add_argument('--split_path', type=str, default='/Users/hstark/projects/ligbind/data/splits/timesplit_test', help='')
+    parser.add_argument('--prank_path', type=str, default='/Users/hstark/projects/p2rank_2.3/prank', help='')
+    parser.add_argument('--results_path', type=str, default='results/tankbind_results', help='')
+    parser.add_argument('--skip_existing', action='store_true', default=False, help='')
+    parser.add_argument('--skip_p2rank', action='store_true', default=False, help='')
+    parser.add_argument('--skip_multiple_pocket_outputs', action='store_true', default=False, help='')
+    parser.add_argument('--device', type=str, default='cpu', help='')
+    parser.add_argument('--num_workers', type=int, default=1, help='')
+    parser.add_argument('--parallel_id', type=int, default=0, help='')
+    parser.add_argument('--parallel_tot', type=int, default=1, help='')
+    args = parser.parse_args()
+
+    device = args.device
+    cache_path = "tankbind_cache"
+    os.makedirs(cache_path, exist_ok=True)
+    os.makedirs(args.results_path, exist_ok=True)
+
+
+
+    logging.basicConfig(level=logging.INFO)
+    model = get_model(0, logging, device)
+    # re-dock model
+    # modelFile = "../saved_models/re_dock.pt"
+    # self-dock model
+    modelFile = f"{tankbind_src_folder}/../saved_models/self_dock.pt"
+
+    model.load_state_dict(torch.load(modelFile, map_location=device))
+    _ = model.eval()
+    batch_size = 5
+    names = read_strings_from_txt(args.split_path)
+    if args.parallel_tot > 1:
+        size = len(names) // args.parallel_tot + 1
+        names = names[args.parallel_id*size:(args.parallel_id+1)*size]
+    rmsds = []
+
+    forward_pass_time = []
+    times_preprocess = []
+    times_inference = []
+    top_10_generation_time = []
+    top_1_generation_time = []
+    start_time = time.time()
+    if not args.skip_p2rank:
+        for name in names:
+            if args.skip_existing and os.path.exists(f'{args.results_path}/{name}/{name}_tankbind_1.sdf'): continue
+            print("Now processing: ", name)
+            protein_path = f'{args.data_dir}/{name}/{name}_protein_processed.pdb'
+            cleaned_protein_path = f"{cache_path}/{name}_protein_tankbind_cleaned.pdb"  # if you change this you also need to change below
+            parser = PDBParser(QUIET=True)
+            s = parser.get_structure(name, protein_path)
+            c = s[0]
+            clean_res_list, ligand_list = save_cleaned_protein(c, cleaned_protein_path)
+
+        with open(f"{cache_path}/pdb_list_p2rank.txt", "w") as out:
+            for name in names:
+                out.write(f"{name}_protein_tankbind_cleaned.pdb\n")
+        cmd = f"bash {args.prank_path} predict {cache_path}/pdb_list_p2rank.txt -o {cache_path}/p2rank -threads 4"
+        os.system(cmd)
+    times_preprocess.append(time.time() - start_time)
+    p2_rank_time = time.time() - start_time
+
+
+
+
+    list_to_parallelize = []
+    for name in tqdm(names):
+        single_preprocess_time = time.time()
+        if args.skip_existing and os.path.exists(f'{args.results_path}/{name}/{name}_tankbind_1.sdf'): continue
+        print("Now processing: ", name)
+        protein_path = f'{args.data_dir}/{name}/{name}_protein_processed.pdb'
+        ligand_path = f"{args.data_dir}/{name}/{name}_ligand.sdf"
+        cleaned_protein_path = f"{cache_path}/{name}_protein_tankbind_cleaned.pdb"  # if you change this you also need to change below
+        rdkit_mol_path = f"{cache_path}/{name}_rdkit_ligand.sdf"
+
+        parser = PDBParser(QUIET=True)
+        s = parser.get_structure(name, protein_path)
+        c = s[0]
+        clean_res_list, ligand_list = save_cleaned_protein(c, cleaned_protein_path)
+        lig, _ = read_mol(f"{args.data_dir}/{name}/{name}_ligand.sdf", f"{args.data_dir}/{name}/{name}_ligand.mol2")
+
+        lig = RemoveHs(lig)
+        smiles = Chem.MolToSmiles(lig)
+        generate_sdf_from_smiles_using_rdkit(smiles, rdkit_mol_path, shift_dis=0)
+
+        parser = PDBParser(QUIET=True)
+        s = parser.get_structure("x", cleaned_protein_path)
+        res_list = list(s.get_residues())
+
+        protein_dict = {}
+        protein_dict[name] = get_protein_feature(res_list)
+        compound_dict = {}
+
+        mol = Chem.MolFromMolFile(rdkit_mol_path)
+        compound_dict[name + f"_{name}" + "_rdkit"] = extract_torchdrug_feature_from_mol(mol, has_LAS_mask=True)
+
+        info = []
+        for compound_name in list(compound_dict.keys()):
+            # use protein center as the block center.
+            com = ",".join([str(a.round(3)) for a in protein_dict[name][0].mean(axis=0).numpy()])
+            info.append([name, compound_name, "protein_center", com])
+
+            p2rankFile = f"{cache_path}/p2rank/{name}_protein_tankbind_cleaned.pdb_predictions.csv"
+            pocket = pd.read_csv(p2rankFile)
+            pocket.columns = pocket.columns.str.strip()
+            pocket_coms = pocket[['center_x', 'center_y', 'center_z']].values
+            for ith_pocket, com in enumerate(pocket_coms):
+                com = ",".join([str(a.round(3)) for a in com])
+                info.append([name, compound_name, f"pocket_{ith_pocket + 1}", com])
+        info = pd.DataFrame(info, columns=['protein_name', 'compound_name', 'pocket_name', 'pocket_com'])
+
+        dataset_path = f"{cache_path}/{name}_dataset/"
+        os.system(f"rm -r {dataset_path}")
+        os.system(f"mkdir -p {dataset_path}")
+        dataset = TankBind_prediction(dataset_path, data=info, protein_dict=protein_dict, compound_dict=compound_dict)
+
+        # dataset = TankBind_prediction(dataset_path)
+        times_preprocess.append(time.time() - single_preprocess_time)
+        single_forward_pass_time = time.time()
+        data_loader = DataLoader(dataset, batch_size=batch_size, follow_batch=['x', 'y', 'compound_pair'], shuffle=False,
+                                 num_workers=0)
+        affinity_pred_list = []
+        y_pred_list = []
+        for data in tqdm(data_loader):
+            data = data.to(device)
+            y_pred, affinity_pred = model(data)
+            affinity_pred_list.append(affinity_pred.detach().cpu())
+            for i in range(data.y_batch.max() + 1):
+                y_pred_list.append((y_pred[data['y_batch'] == i]).detach().cpu())
+
+        affinity_pred_list = torch.cat(affinity_pred_list)
+        forward_pass_time.append(time.time() - single_forward_pass_time)
+        output_info = copy.deepcopy(dataset.data)
+        output_info['affinity'] = affinity_pred_list
+        output_info['dataset_index'] = range(len(output_info))
+        output_info_sorted = output_info.sort_values('affinity', ascending=False)
+
+
+        result_folder = f'{args.results_path}/{name}'
+        os.makedirs(result_folder, exist_ok=True)
+        output_info_sorted.to_csv(f"{result_folder}/output_info_sorted_by_affinity.csv")
+
+        if not args.skip_multiple_pocket_outputs:
+            for idx, (dataframe_idx, line) in enumerate(copy.deepcopy(output_info_sorted).iterrows()):
+                single_top10_generation_time = time.time()
+                pocket_name = line['pocket_name']
+                compound_name = line['compound_name']
+                ligandName = compound_name.split("_")[1]
+                coords = dataset[dataframe_idx].coords.to('cpu')
+                protein_nodes_xyz = dataset[dataframe_idx].node_xyz.to('cpu')
+                n_compound = coords.shape[0]
+                n_protein = protein_nodes_xyz.shape[0]
+                y_pred = y_pred_list[dataframe_idx].reshape(n_protein, n_compound).to('cpu')
+                y = dataset[dataframe_idx].dis_map.reshape(n_protein, n_compound).to('cpu')
+                compound_pair_dis_constraint = torch.cdist(coords, coords)
+                mol = Chem.MolFromMolFile(rdkit_mol_path)
+                LAS_distance_constraint_mask = get_LAS_distance_constraint_mask(mol).bool()
+                pred_dist_info = get_info_pred_distance(coords, y_pred, protein_nodes_xyz, compound_pair_dis_constraint,
+                                              LAS_distance_constraint_mask=LAS_distance_constraint_mask,
+                                              n_repeat=1, show_progress=False)
+
+                toFile = f'{result_folder}/{name}_tankbind_{idx}.sdf'
+                new_coords = pred_dist_info.sort_values("loss")['coords'].iloc[0].astype(np.double)
+                write_with_new_coords(mol, new_coords, toFile)
+                if idx < 10:
+                    top_10_generation_time.append(time.time() - single_top10_generation_time)
+                if idx == 0:
+                    top_1_generation_time.append(time.time() - single_top10_generation_time)
+
+        output_info_chosen = copy.deepcopy(dataset.data)
+        output_info_chosen['affinity'] = affinity_pred_list
+        output_info_chosen['dataset_index'] = range(len(output_info_chosen))
+        chosen = output_info_chosen.loc[
+            output_info_chosen.groupby(['protein_name', 'compound_name'], sort=False)['affinity'].agg(
+                'idxmax')].reset_index()
+
+        list_to_parallelize.append((dataset, y_pred_list, chosen, rdkit_mol_path, result_folder, name))
+
+    chosen_generation_start_time = time.time()
+    if args.num_workers > 1:
+        p = Pool(args.num_workers, maxtasksperchild=1)
+        p.__enter__()
+    with tqdm(total=len(list_to_parallelize), desc=f'running optimization {i}/{len(list_to_parallelize)}') as pbar:
+        map_fn = p.imap_unordered if args.num_workers > 1 else map
+        for t in map_fn(parallel_save_prediction, list_to_parallelize):
+            pbar.update()
+    if args.num_workers > 1: p.__exit__(None, None, None)
+    chosen_generation_time = time.time() - chosen_generation_start_time
+    """
+        lig, _ = read_mol(f"{args.data_dir}/{name}/{name}_ligand.sdf", f"{args.data_dir}/{name}/{name}_ligand.mol2")
+        sm = Chem.MolToSmiles(lig)
+        m_order = list(lig.GetPropsAsDict(includePrivate=True, includeComputed=True)['_smilesAtomOutputOrder'])
+        lig = Chem.RenumberAtoms(lig, m_order)
+        lig = Chem.RemoveAllHs(lig)
+        lig = RemoveHs(lig)
+        true_ligand_pos = np.array(lig.GetConformer().GetPositions())
+    
+        toFile = f'{result_folder}/{name}_tankbind_chosen.sdf'
+        mol_pred, _ = read_mol(toFile, None)
+        sm = Chem.MolToSmiles(mol_pred)
+        m_order = list(mol_pred.GetPropsAsDict(includePrivate=True, includeComputed=True)['_smilesAtomOutputOrder'])
+        mol_pred = Chem.RenumberAtoms(mol_pred, m_order)
+        mol_pred = RemoveHs(mol_pred)
+        mol_pred_pos = np.array(mol_pred.GetConformer().GetPositions())
+        rmsds.append(np.sqrt(((true_ligand_pos - mol_pred_pos) ** 2).sum(axis=1).mean(axis=0)))
+        print(np.sqrt(((true_ligand_pos - mol_pred_pos) ** 2).sum(axis=1).mean(axis=0)))
+    """
+    forward_pass_time  = np.array(forward_pass_time).sum()
+    times_preprocess  = np.array(times_preprocess).sum()
+    times_inference  = np.array(times_inference).sum()
+    top_10_generation_time  = np.array(top_10_generation_time).sum()
+    top_1_generation_time  = np.array(top_1_generation_time).sum()
+
+    rmsds = np.array(rmsds)
+
+    print(f'forward_pass_time: {forward_pass_time}')
+    print(f'times_preprocess: {times_preprocess}')
+    print(f'times_inference: {times_inference}')
+    print(f'top_10_generation_time: {top_10_generation_time}')
+    print(f'top_1_generation_time: {top_1_generation_time}')
+    print(f'chosen_generation_time: {chosen_generation_time}')
+    print(f'rmsds_below_2: {(100 * (rmsds < 2).sum() / len(rmsds))}')
+    print(f'p2rank Time: {p2_rank_time}')
+    print(
+        f'total_time: '
+        f'{forward_pass_time + times_preprocess + times_inference + top_10_generation_time + top_1_generation_time + p2_rank_time}')
+
+    with open(os.path.join(args.results_path, 'tankbind_log.log'), 'w') as file:
+        file.write(f'forward_pass_time: {forward_pass_time}')
+        file.write(f'times_preprocess: {times_preprocess}')
+        file.write(f'times_inference: {times_inference}')
+        file.write(f'top_10_generation_time: {top_10_generation_time}')
+        file.write(f'top_1_generation_time: {top_1_generation_time}')
+        file.write(f'rmsds_below_2: {(100 * (rmsds < 2).sum() / len(rmsds))}')
+        file.write(f'p2rank Time: {p2_rank_time}')
+        file.write(f'total_time: {forward_pass_time + times_preprocess + times_inference + top_10_generation_time + top_1_generation_time + p2_rank_time}')

data/protein_ligand_example_csv.csv

@@ -0,0 +1,2 @@
+protein_path,ligand
+examples/1cbr_protein.pdb,CCCC

data/splits/timesplit_no_lig_overlap_train

@@ -0,0 +1,16379 @@
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data/splits/timesplit_test

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data/splits/timesplit_test_no_rec_overlap

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environment.yml

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+name: diffdock
+channels:
+  - pytorch
+  - defaults
+dependencies:
+  - blas=1.0
+  - brotlipy=0.7.0
+  - bzip2=1.0.8
+  - ca-certificates=2022.07.19
+  - certifi=2022.9.14
+  - cffi=1.15.1
+  - charset-normalizer=2.0.4
+  - cryptography=37.0.1
+  - ffmpeg=4.3
+  - freetype=2.11.0
+  - gettext=0.21.0
+  - giflib=5.2.1
+  - gmp=6.2.1
+  - gnutls=3.6.15
+  - icu=58.2
+  - idna=3.3
+  - intel-openmp=2021.4.0
+  - jpeg=9e
+  - lame=3.100
+  - lcms2=2.12
+  - lerc=3.0
+  - libcxx=14.0.6
+  - libdeflate=1.8
+  - libffi=3.3
+  - libiconv=1.16
+  - libidn2=2.3.2
+  - libpng=1.6.37
+  - libtasn1=4.16.0
+  - libtiff=4.4.0
+  - libunistring=0.9.10
+  - libwebp=1.2.2
+  - libwebp-base=1.2.2
+  - libxml2=2.9.14
+  - llvm-openmp=14.0.6
+  - lz4-c=1.9.3
+  - mkl=2021.4.0
+  - mkl-service=2.4.0
+  - mkl_fft=1.3.1
+  - mkl_random=1.2.2
+  - ncurses=6.3
+  - nettle=3.7.3
+  - numpy=1.23.1
+  - numpy-base=1.23.1
+  - openh264=2.1.1
+  - openssl=1.1.1q
+  - pillow=9.2.0
+  - pip=22.2.2
+  - pycparser=2.21
+  - pyopenssl=22.0.0
+  - pysocks=1.7.1
+  - python=3.9.13
+  - pytorch=1.12.1
+  - readline=8.1.2
+  - requests=2.28.1
+  - setuptools=63.4.1
+  - six=1.16.0
+  - sqlite=3.39.3
+  - tk=8.6.12
+  - torchaudio=0.12.1
+  - torchvision=0.13.1
+  - typing_extensions=4.3.0
+  - tzdata=2022c
+  - urllib3=1.26.11
+  - wheel=0.37.1
+  - xz=5.2.6
+  - zlib=1.2.12
+  - zstd=1.5.2
+  - pip:
+    - biopandas==0.4.1
+    - biopython==1.79
+    - e3nn==0.5.0
+    - jinja2==3.1.2
+    - joblib==1.2.0
+    - markupsafe==2.1.1
+    - mpmath==1.2.1
+    - networkx==2.8.7
+    - opt-einsum==3.3.0
+    - opt-einsum-fx==0.1.4
+    - packaging==21.3
+    - pandas==1.5.0
+    - pyaml==21.10.1
+    - pyparsing==3.0.9
+    - python-dateutil==2.8.2
+    - pytz==2022.4
+    - pyyaml==6.0
+    - rdkit-pypi==2022.3.5
+    - scikit-learn==1.1.2
+    - scipy==1.9.1
+    - spyrmsd==0.5.2
+    - sympy==1.11.1
+    - threadpoolctl==3.1.0
+    - torch-cluster==1.6.0
+    - torch-geometric==2.1.0.post1
+    - torch-scatter==2.0.9
+    - torch-sparse==0.6.15
+    - torch-spline-conv==1.2.1
+    - tqdm==4.64.1

examples/1a46_ligand.sdf

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+ 28 70  1  0  0  0
+ 29 71  1  0  0  0
+ 29 72  1  0  0  0
+ 29 73  1  0  0  0
+ 30 74  1  0  0  0
+ 33 75  1  0  0  0
+ 34 76  1  0  0  0
+ 34 77  1  0  0  0
+ 35 78  1  0  0  0
+ 35 79  1  0  0  0
+ 36 80  1  0  0  0
+ 36 81  1  0  0  0
+ 37 82  1  0  0  0
+ 37 83  1  0  0  0
+ 38 84  1  0  0  0
+ 38 85  1  0  0  0
+M  END
+$$$$

examples/1cbr_ligand.sdf

@@ -0,0 +1,119 @@
+1cbr_ligand
+
+Created by X-TOOL on Fri Nov 18 12:01:53 2016
+ 49 49  0  0  0  0  0  0  0  0999 V2000
+    5.0920    2.4270  -10.7940  C 0  0  0  1  0  4
+    6.0790    1.2390  -10.8790  C 0  0  0  3  0  4
+    7.4570    1.5880  -11.3400  C 0  0  0  3  0  4
+    8.1090    2.6160  -10.4790  C 0  0  0  3  0  4
+    7.1710    3.7700  -10.1040  C 0  0  0  1  0  3
+    5.8090    3.6640  -10.1590  C 0  0  0  1  0  3
+    4.8670    4.7410   -9.7870  C 0  0  0  2  0  3
+    5.0090    5.6850   -8.8490  C 0  0  0  2  0  3
+    4.0490    6.7120   -8.5120  C 0  0  0  1  0  3
+    4.3830    7.6020   -7.5550  C 0  0  0  2  0  3
+    3.5130    8.6700   -7.1050  C 0  0  0  2  0  3
+    3.9620    9.5090   -6.1670  C 0  0  0  2  0  3
+    3.1640   10.5920   -5.6370  C 0  0  0  1  0  3
+    3.7030   11.3990   -4.6890  C 0  0  0  2  0  3
+    3.0710   12.5430   -4.0160  C 0  5  0  1  0  3
+    3.9070    2.0000   -9.9190  C 0  0  0  4  0  4
+    4.5820    2.7980  -12.2130  C 0  0  0  4  0  4
+    7.9800    4.9390   -9.5360  C 0  0  0  4  0  4
+    2.7160    6.8010   -9.2660  C 0  0  0  4  0  4
+    1.7300   10.7780   -6.1620  C 0  0  0  4  0  4
+    2.5240   13.4330   -4.7040  O 0  0  0  1  0  1
+    3.0900   12.6020   -2.7660  O 0  0  0  1  0  1
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+    6.1586    0.7905   -9.8778  H 0  0  0  1  0  1
+    7.3965    1.9765  -12.3673  H 0  0  0  1  0  1
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+    3.9353    4.7700  -10.3501  H 0  0  0  1  0  1
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+    4.9725    9.3712   -5.7852  H 0  0  0  1  0  1
+    4.7256   11.1723   -4.3911  H 0  0  0  1  0  1
+    3.1893    2.8302   -9.8432  H 0  0  0  1  0  1
+    4.2693    1.7357   -8.9146  H 0  0  0  1  0  1
+    3.4124    1.1280  -10.3717  H 0  0  0  1  0  1
+    5.4325    3.1041  -12.8399  H 0  0  0  1  0  1
+    3.8636    3.6277  -12.1392  H 0  0  0  1  0  1
+    4.0887    1.9250  -12.6652  H 0  0  0  1  0  1
+    7.2992    5.7611   -9.2702  H 0  0  0  1  0  1
+    8.6994    5.2884  -10.2913  H 0  0  0  1  0  1
+    8.5226    4.6076   -8.6384  H 0  0  0  1  0  1
+    2.6523    5.9808   -9.9962  H 0  0  0  1  0  1
+    2.6558    7.7653   -9.7917  H 0  0  0  1  0  1
+    1.8841    6.7206   -8.5508  H 0  0  0  1  0  1
+    1.5151   10.0113   -6.9209  H 0  0  0  1  0  1
+    1.6308   11.7769   -6.6117  H 0  0  0  1  0  1
+    1.0187   10.6787   -5.3288  H 0  0  0  1  0  1
+  1  2  1  0  0  1
+  1  6  1  0  0  1
+  1 16  1  0  0  2
+  1 17  1  0  0  2
+  2  3  1  0  0  1
+  3  4  1  0  0  1
+  4  5  1  0  0  1
+  5  6  2  0  0  1
+  5 18  1  0  0  2
+  6  7  1  0  0  2
+  7  8  2  0  0  2
+  8  9  1  0  0  2
+  9 10  2  0  0  2
+  9 19  1  0  0  2
+ 10 11  1  0  0  2
+ 11 12  2  0  0  2
+ 12 13  1  0  0  2
+ 13 14  2  0  0  2
+ 13 20  1  0  0  2
+ 14 15  1  0  0  2
+ 15 21  2  0  0  2
+ 15 22  2  0  0  2
+  2 23  1  0  0  2
+  2 24  1  0  0  2
+  3 25  1  0  0  2
+  3 26  1  0  0  2
+  4 27  1  0  0  2
+  4 28  1  0  0  2
+  7 29  1  0  0  2
+  8 30  1  0  0  2
+ 10 31  1  0  0  2
+ 11 32  1  0  0  2
+ 12 33  1  0  0  2
+ 14 34  1  0  0  2
+ 16 35  1  0  0  2
+ 16 36  1  0  0  2
+ 16 37  1  0  0  2
+ 17 38  1  0  0  2
+ 17 39  1  0  0  2
+ 17 40  1  0  0  2
+ 18 41  1  0  0  2
+ 18 42  1  0  0  2
+ 18 43  1  0  0  2
+ 19 44  1  0  0  2
+ 19 45  1  0  0  2
+ 19 46  1  0  0  2
+ 20 47  1  0  0  2
+ 20 48  1  0  0  2
+ 20 49  1  0  0  2
+M  END
+> <MOLECULAR_FORMULA>
+C20H27O2
+
+> <MOLECULAR_WEIGHT>
+299.2
+
+> <NUM_HB_ATOMS>
+2  
+
+> <NUM_ROTOR>
+0  
+
+> <XLOGP2>
+3.40 
+
+$$$$

examples/6ahs_ligand.sdf

@@ -0,0 +1,97 @@
+6ahs_ligand
+  -I-interpret- 
+
+ 44 47  0  0  0  0  0  0  0  0999 V2000
+  -12.3770   21.8080   53.5010 C   0  0  0  0  0
+  -13.7990   19.8830   53.8470 C   0  0  0  0  0
+  -12.7140   27.6870   53.9730 C   0  0  0  0  0
+  -14.5790   28.9380   54.4000 C   0  0  0  0  0
+  -15.0000   26.1900   58.7440 C   0  0  0  0  0
+  -10.5140   22.1070   55.1220 O   0  0  0  0  0
+  -11.1780   22.7720   54.0310 S   0  0  0  0  0
+  -10.2040   22.9400   53.0000 O   0  0  0  0  0
+  -12.7440   20.6930   54.2570 C   0  0  0  0  0
+  -13.0670   22.0720   52.3070 C   0  0  0  0  0
+  -12.6220   23.4480   51.2580 Cl  0  0  0  0  0
+  -14.1300   21.2760   51.9160 C   0  0  0  0  0
+  -14.4890   20.1760   52.6790 C   0  0  0  0  0
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+  -13.0050   25.7840   55.5680 C   0  0  0  0  0
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+  -16.0210   29.3400   54.4520 C   0  0  0  0  0
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+  -11.4030   25.4307   52.6817 H   0  0  0  0  0
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+  -11.5409   29.4916   54.5336 H   0  0  0  0  0
+  -11.9880   28.5603   55.8854 H   0  0  0  0  0
+  -13.7372   30.5065   55.4522 H   0  0  0  0  0
+  -16.4083   29.4311   53.4372 H   0  0  0  0  0
+  -16.5888   28.5825   54.9924 H   0  0  0  0  0
+  -16.1116   30.2979   54.9641 H   0  0  0  0  0
+  7  1  1  0  0  0
+  1  9  4  0  0  0
+  1 10  4  0  0  0
+  9  2  4  0  0  0
+  2 13  4  0  0  0
+ 22  3  1  0  0  0
+  3 23  1  0  0  0
+  3 24  1  0  0  0
+ 23  4  2  0  0  0
+  4 25  1  0  0  0
+  4 26  1  0  0  0
+ 20  5  1  0  0  0
+  7  6  2  0  0  0
+  7  8  2  0  0  0
+ 14  7  1  0  0  0
+ 10 11  1  0  0  0
+ 10 12  4  0  0  0
+ 12 13  4  0  0  0
+ 15 14  4  0  0  0
+ 16 14  4  0  0  0
+ 22 15  4  0  0  0
+ 16 17  4  0  0  0
+ 18 16  4  0  0  0
+ 17 21  4  0  0  0
+ 19 18  4  0  0  0
+ 18 22  4  0  0  0
+ 19 20  4  0  0  0
+ 20 21  4  0  0  0
+ 24 25  1  0  0  0
+  2 27  1  0  0  0
+  3 28  1  0  0  0
+  5 29  1  0  0  0
+  5 30  1  0  0  0
+  5 31  1  0  0  0
+  9 32  1  0  0  0
+ 12 33  1  0  0  0
+ 13 34  1  0  0  0
+ 15 35  1  0  0  0
+ 17 36  1  0  0  0
+ 19 37  1  0  0  0
+ 21 38  1  0  0  0
+ 24 39  1  0  0  0
+ 24 40  1  0  0  0
+ 25 41  1  0  0  0
+ 26 42  1  0  0  0
+ 26 43  1  0  0  0
+ 26 44  1  0  0  0
+M  END
+$$$$

examples/6moa_ligand.sdf

@@ -0,0 +1,71 @@
+
+     RDKit          3D
+
+ 30 35  0  0  0  0  0  0  0  0999 V2000
+    6.2968   58.1936   -6.7517 C   0  0  0  0  0  0  0  0  0  0  0  0
+    5.1035   58.8201   -6.1288 C   0  0  0  0  0  0  0  0  0  0  0  0
+    3.7958   58.4707   -6.4904 C   0  0  0  0  0  0  0  0  0  0  0  0
+    2.7073   59.0722   -5.8919 C   0  0  0  0  0  0  0  0  0  0  0  0
+    2.9189   60.0304   -4.9246 C   0  0  0  0  0  0  0  0  0  0  0  0
+    4.2268   60.3805   -4.5624 C   0  0  0  0  0  0  0  0  0  0  0  0
+    5.3090   59.7770   -5.1629 C   0  0  0  0  0  0  0  0  0  0  0  0
+    6.6607   60.1383   -4.7890 C   0  0  0  0  0  0  0  0  0  0  0  0
+    7.3183   61.1933   -5.4064 C   0  0  0  0  0  0  0  0  0  0  0  0
+    8.5883   61.5725   -5.0913 C   0  0  0  0  0  0  0  0  0  0  0  0
+    9.2881   60.8940   -4.1110 C   0  0  0  0  0  0  0  0  0  0  0  0
+    8.6560   59.8464   -3.4872 C   0  0  0  0  0  0  0  0  0  0  0  0
+    7.3711   59.4625   -3.8057 C   0  0  0  0  0  0  0  0  0  0  0  0
+    7.0027   58.4275   -3.0563 N   0  0  0  0  0  0  0  0  0  0  0  0
+    8.0627   58.1502   -2.2536 C   0  0  0  0  0  0  0  0  0  0  0  0
+    9.0581   59.0122   -2.5200 N   0  0  0  0  0  0  0  0  0  0  0  0
+    8.1493   57.0716   -1.2283 C   0  0  0  0  0  0  0  0  0  0  0  0
+    9.5766   56.5796   -1.0253 C   0  0  0  0  0  0  0  0  0  0  0  0
+    8.8832   57.3389    0.0412 C   0  0  0  0  0  0  0  0  0  0  0  0
+    9.2291   62.6963   -5.7800 C   0  0  0  0  0  0  0  0  0  0  0  0
+    9.7347   63.8265   -5.1605 C   0  0  0  0  0  0  0  0  0  0  0  0
+   10.2222   64.5824   -6.1570 N   0  0  0  0  0  0  0  0  0  0  0  0
+   10.0372   63.9781   -7.2947 O   0  0  0  0  0  0  0  0  0  0  0  0
+    9.4349   62.8237   -7.1167 C   0  0  0  0  0  0  0  0  0  0  0  0
+    9.0345   61.8184   -8.1338 C   0  0  0  0  0  0  0  0  0  0  0  0
+    9.6965   64.0557   -3.7051 C   0  0  0  0  0  0  0  0  0  0  0  0
+    4.4459   61.3389   -3.5948 C   0  0  0  0  0  0  0  0  0  0  0  0
+    3.3642   61.9433   -2.9935 C   0  0  0  0  0  0  0  0  0  0  0  0
+    2.0968   61.5645   -3.3844 C   0  0  0  0  0  0  0  0  0  0  0  0
+    1.8948   60.6282   -4.3295 N   0  0  0  0  0  0  0  0  0  0  0  0
+  1  2  1  0
+  2  3  2  0
+  3  4  1  0
+  4  5  2  0
+  5  6  1  0
+  6  7  2  0
+  7  8  1  0
+  8  9  2  0
+  9 10  1  0
+ 10 11  2  0
+ 11 12  1  0
+ 12 13  2  0
+ 13 14  1  0
+ 14 15  2  0
+ 15 16  1  0
+ 15 17  1  0
+ 17 18  1  0
+ 18 19  1  0
+ 10 20  1  0
+ 20 21  1  0
+ 21 22  2  0
+ 22 23  1  0
+ 23 24  1  0
+ 24 25  1  0
+ 21 26  1  0
+  6 27  1  0
+ 27 28  2  0
+ 28 29  1  0
+ 29 30  2  0
+  7  2  1  0
+ 30  5  1  0
+ 13  8  1  0
+ 16 12  1  0
+ 19 17  1  0
+ 24 20  2  0
+M  END
+$$$$

examples/6o5u_ligand.sdf

@@ -0,0 +1,154 @@
+6o5u_ligand
+  -I-interpret- 
+
+ 73 75  0  0  0  0  0  0  0  0999 V2000
+  108.6730  -18.4700   -7.8360 C   0  0  0  0  0
+  107.3110  -18.9740   -7.3680 C   0  0  0  0  0
+  107.4800  -20.3570   -6.7640 C   0  0  0  0  0
+  108.0820  -21.2810   -7.8150 C   0  0  0  0  0
+  109.3440  -20.6380   -8.3930 C   0  0  0  0  0
+  109.7960  -21.4700   -9.5890 C   0  0  0  0  0
+  112.5990  -15.8100   -5.7750 C   0  0  0  0  0
+  112.1040  -15.6660   -7.2080 C   0  0  0  0  0
+  110.7540  -16.3630   -7.2880 C   0  0  0  0  0
+  110.8240  -17.8460   -6.8990 C   0  0  0  0  0
+  111.5110  -18.0160   -5.5370 C   0  0  0  0  0
+  112.8430  -17.2770   -5.4680 C   0  0  0  0  0
+  112.8110  -13.7240   -8.4160 C   0  0  0  0  0
+  112.9750  -12.2640   -8.0080 C   0  0  0  0  0
+  111.6610  -11.5000   -8.1330 C   0  0  0  0  0
+  111.2120  -11.6530   -9.5690 C   0  0  0  0  0
+  111.1210  -13.1180   -9.9350 C   0  0  0  0  0
+  110.8030  -13.1430  -11.4220 C   0  0  0  0  0
+  110.9760  -20.8510  -10.1750 N   0  3  0  0  0
+  111.7470  -19.4450   -5.3220 N   0  3  0  0  0
+  113.8400  -15.0670   -5.6100 N   0  3  0  0  0
+  111.8410  -10.0810   -7.8770 N   0  3  0  0  0
+  109.1180  -19.3280   -8.8520 O   0  0  0  0  0
+  106.8170  -18.1010   -6.3680 O   0  0  0  0  0
+  106.1940  -20.8240   -6.3760 O   0  0  0  0  0
+  108.4250  -22.5370   -7.2480 O   0  0  0  0  0
+  109.5510  -18.4650   -6.7100 O   0  0  0  0  0
+  110.3630  -16.2700   -8.6430 O   0  0  0  0  0
+  111.9040  -14.2970   -7.4880 O   0  0  0  0  0
+  112.3420  -13.8090   -9.7580 O   0  0  0  0  0
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+  113.7270  -11.8166   -8.6580 H   0  0  0  0  0
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+ 33 73  1  0  0  0
+M  END
+$$$$

examples/6w70_ligand.sdf

@@ -0,0 +1,170 @@
+GG2
+  ModelServer 0.9.9
+
+ 59 63  0  0  0  0  0  0  0  0  0
+    8.5320    2.5640    9.2510 C   0  0  0  0  0  0  0  0  0  0  0  0
+    9.4650    2.7080   10.2660 C   0  0  0  0  0  0  0  0  0  0  0  0
+    9.8540    3.9770   10.6370 C   0  0  0  0  0  0  0  0  0  0  0  0
+    8.0890    1.3510    8.8580 O   0  0  0  0  0  0  0  0  0  0  0  0
+    9.5960    6.6430   11.6760 N   0  0  0  0  0  0  0  0  0  0  0  0
+    9.8460    9.2980    6.4400 C   0  0  0  0  0  0  0  0  0  0  0  0
+   10.0780    7.7570    8.2120 C   0  0  0  0  0  0  0  0  0  0  0  0
+    9.7660    7.9380   11.8540 C   0  0  0  0  0  0  0  0  0  0  0  0
+    9.7240    8.5870   13.2020 C   0  0  0  0  0  0  0  0  0  0  0  0
+   10.0020    8.5590   10.5660 C   0  0  0  0  0  0  0  0  0  0  0  0
+    9.8890    7.4660    9.6420 C   0  0  0  0  0  0  0  0  0  0  0  0
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+    8.7430    9.5470    1.6160 C   0  0  0  0  0  0  0  0  0  0  0  0
+   10.5460   11.1730    1.8740 C   0  0  0  0  0  0  0  0  0  0  0  0
+   10.3530   11.5920    0.4200 C   0  0  0  0  0  0  0  0  0  0  0  0
+    8.7550    9.8660    5.9000 C   0  0  0  0  0  0  0  0  0  0  0  0
+    9.4480   10.0660    8.7730 C   0  0  0  0  0  0  0  0  0  0  0  0
+    9.4050    5.1120    9.9700 C   0  0  0  0  0  0  0  0  0  0  0  0
+    8.4880    4.8930    8.9450 C   0  0  0  0  0  0  0  0  0  0  0  0
+    8.0710    3.6360    8.5650 C   0  0  0  0  0  0  0  0  0  0  0  0
+    9.7130    6.3620   10.3560 N   0  0  0  0  0  0  0  0  0  0  0  0
+    9.8530    9.0000    7.8320 N   0  0  0  0  0  0  0  0  0  0  0  0
+   10.8940    9.0320    5.6080 C   0  0  0  0  0  0  0  0  0  0  0  0
+    9.6180   10.1750    2.3580 N   0  0  0  0  0  0  0  0  0  0  0  0
+    8.6640   10.1500    4.5380 C   0  0  0  0  0  0  0  0  0  0  0  0
+   10.3590    6.8700    7.5200 O   0  0  0  0  0  0  0  0  0  0  0  0
+   10.1420   10.3580   -0.4200 C   0  0  0  0  0  0  0  0  0  0  0  0
+    7.9090    8.8200    2.0390 O   0  0  0  0  0  0  0  0  0  0  0  0
+    8.8940    9.7380    0.1160 C   0  0  0  0  0  0  0  0  0  0  0  0
+   10.1430    9.7080   13.3960 O   0  0  0  0  0  0  0  0  0  0  0  0
+    9.3030    7.8290   14.1650 N   0  0  0  0  0  0  0  0  0  0  0  0
+   10.2120    9.9570   10.1020 C   0  0  0  0  0  0  0  0  0  0  0  0
+    9.8220    1.9610   10.6890 H   0  0  0  0  0  0  0  0  0  0  0  0
+   10.4350    4.0780   11.3560 H   0  0  0  0  0  0  0  0  0  0  0  0
+   11.5100    9.1080    3.6700 H   0  0  0  0  0  0  0  0  0  0  0  0
+    9.6620    0.2420    9.2830 H   0  0  0  0  0  0  0  0  0  0  0  0
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+   11.1570   10.1290    9.9640 H   0  0  0  0  0  0  0  0  0  0  0  0
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+  1  2  1  0  0  0  0
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+  4 13  1  0  0  0  0
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+  6 25  2  0  0  0  0
+  6 18  1  0  0  0  0
+  6 24  1  0  0  0  0
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+  7 11  1  0  0  0  0
+  8 10  1  0  0  0  0
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+ 15 31  1  0  0  0  0
+ 15 30  2  0  0  0  0
+ 15 26  1  0  0  0  0
+ 16 17  1  0  0  0  0
+ 16 26  1  0  0  0  0
+ 16 41  1  0  0  0  0
+ 16 42  1  0  0  0  0
+ 17 29  1  0  0  0  0
+ 17 43  1  0  0  0  0
+ 17 44  1  0  0  0  0
+ 18 27  2  0  0  0  0
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+ 20 23  1  0  0  0  0
+ 20 21  2  0  0  0  0
+ 21 22  1  0  0  0  0
+ 21 48  1  0  0  0  0
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+ 25 50  1  0  0  0  0
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+ 33 56  1  0  0  0  0
+ 33 57  1  0  0  0  0
+ 34 58  1  0  0  0  0
+ 34 59  1  0  0  0  0
+M  END
+> <model_server_result.job_id>
+jhYbiAhZngEaKGexZ2WIZQ
+
+> <model_server_result.datetime_utc>
+2022-10-10 01:28:25
+
+> <model_server_result.server_version>
+0.9.9
+
+> <model_server_result.query_name>
+ligand
+
+> <model_server_result.source_id>
+pdb-bcif
+
+> <model_server_result.entry_id>
+6w70
+
+> <model_server_params.name>
+atom_site
+
+> <model_server_params.value>
+{"label_asym_id":"E","auth_seq_id":303}
+
+> <model_server_stats.io_time_ms>
+49
+
+> <model_server_stats.parse_time_ms>
+60
+
+> <model_server_stats.create_model_time_ms>
+10
+
+> <model_server_stats.query_time_ms>
+220
+
+> <model_server_stats.encode_time_ms>
+0
+
+> <model_server_stats.element_count>
+59
+
+$$$$

train.py

@@ -0,0 +1,158 @@
+import copy
+import math
+import os
+from functools import partial
+
+import wandb
+import torch
+torch.multiprocessing.set_sharing_strategy('file_system')
+
+import resource
+rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
+resource.setrlimit(resource.RLIMIT_NOFILE, (64000, rlimit[1]))
+
+import yaml
+
+from utils.diffusion_utils import t_to_sigma as t_to_sigma_compl
+from datasets.pdbbind import construct_loader
+from utils.parsing import parse_train_args
+from utils.training import train_epoch, test_epoch, loss_function, inference_epoch
+from utils.utils import save_yaml_file, get_optimizer_and_scheduler, get_model, ExponentialMovingAverage
+
+
+def train(args, model, optimizer, scheduler, ema_weights, train_loader, val_loader, t_to_sigma, run_dir):
+    best_val_loss = math.inf
+    best_val_inference_value = math.inf if args.inference_earlystop_goal == 'min' else 0
+    best_epoch = 0
+    best_val_inference_epoch = 0
+    loss_fn = partial(loss_function, tr_weight=args.tr_weight, rot_weight=args.rot_weight,
+                      tor_weight=args.tor_weight, no_torsion=args.no_torsion)
+
+    print("Starting training...")
+    for epoch in range(args.n_epochs):
+        if epoch % 5 == 0: print("Run name: ", args.run_name)
+        logs = {}
+        train_losses = train_epoch(model, train_loader, optimizer, device, t_to_sigma, loss_fn, ema_weights)
+        print("Epoch {}: Training loss {:.4f}  tr {:.4f}   rot {:.4f}   tor {:.4f}"
+              .format(epoch, train_losses['loss'], train_losses['tr_loss'], train_losses['rot_loss'],
+                      train_losses['tor_loss']))
+
+        ema_weights.store(model.parameters())
+        if args.use_ema: ema_weights.copy_to(model.parameters()) # load ema parameters into model for running validation and inference
+        val_losses = test_epoch(model, val_loader, device, t_to_sigma, loss_fn, args.test_sigma_intervals)
+        print("Epoch {}: Validation loss {:.4f}  tr {:.4f}   rot {:.4f}   tor {:.4f}"
+              .format(epoch, val_losses['loss'], val_losses['tr_loss'], val_losses['rot_loss'], val_losses['tor_loss']))
+
+        if args.val_inference_freq != None and (epoch + 1) % args.val_inference_freq == 0:
+            inf_metrics = inference_epoch(model, val_loader.dataset.complex_graphs[:args.num_inference_complexes], device, t_to_sigma, args)
+            print("Epoch {}: Val inference rmsds_lt2 {:.3f} rmsds_lt5 {:.3f}"
+                  .format(epoch, inf_metrics['rmsds_lt2'], inf_metrics['rmsds_lt5']))
+            logs.update({'valinf_' + k: v for k, v in inf_metrics.items()}, step=epoch + 1)
+
+        if not args.use_ema: ema_weights.copy_to(model.parameters())
+        ema_state_dict = copy.deepcopy(model.module.state_dict() if device.type == 'cuda' else model.state_dict())
+        ema_weights.restore(model.parameters())
+
+        if args.wandb:
+            logs.update({'train_' + k: v for k, v in train_losses.items()})
+            logs.update({'val_' + k: v for k, v in val_losses.items()})
+            logs['current_lr'] = optimizer.param_groups[0]['lr']
+            wandb.log(logs, step=epoch + 1)
+
+        state_dict = model.module.state_dict() if device.type == 'cuda' else model.state_dict()
+        if args.inference_earlystop_metric in logs.keys() and \
+                (args.inference_earlystop_goal == 'min' and logs[args.inference_earlystop_metric] <= best_val_inference_value or
+                 args.inference_earlystop_goal == 'max' and logs[args.inference_earlystop_metric] >= best_val_inference_value):
+            best_val_inference_value = logs[args.inference_earlystop_metric]
+            best_val_inference_epoch = epoch
+            torch.save(state_dict, os.path.join(run_dir, 'best_inference_epoch_model.pt'))
+            torch.save(ema_state_dict, os.path.join(run_dir, 'best_ema_inference_epoch_model.pt'))
+        if val_losses['loss'] <= best_val_loss:
+            best_val_loss = val_losses['loss']
+            best_epoch = epoch
+            torch.save(state_dict, os.path.join(run_dir, 'best_model.pt'))
+            torch.save(ema_state_dict, os.path.join(run_dir, 'best_ema_model.pt'))
+
+        if scheduler:
+            if args.val_inference_freq is not None:
+                scheduler.step(best_val_inference_value)
+            else:
+                scheduler.step(val_losses['loss'])
+
+        torch.save({
+            'epoch': epoch,
+            'model': state_dict,
+            'optimizer': optimizer.state_dict(),
+            'ema_weights': ema_weights.state_dict(),
+        }, os.path.join(run_dir, 'last_model.pt'))
+
+    print("Best Validation Loss {} on Epoch {}".format(best_val_loss, best_epoch))
+    print("Best inference metric {} on Epoch {}".format(best_val_inference_value, best_val_inference_epoch))
+
+
+def main_function():
+    args = parse_train_args()
+    if args.config:
+        config_dict = yaml.load(args.config, Loader=yaml.FullLoader)
+        arg_dict = args.__dict__
+        for key, value in config_dict.items():
+            if isinstance(value, list):
+                for v in value:
+                    arg_dict[key].append(v)
+            else:
+                arg_dict[key] = value
+        args.config = args.config.name
+    assert (args.inference_earlystop_goal == 'max' or args.inference_earlystop_goal == 'min')
+    if args.val_inference_freq is not None and args.scheduler is not None:
+        assert (args.scheduler_patience > args.val_inference_freq) # otherwise we will just stop training after args.scheduler_patience epochs
+    if args.cudnn_benchmark:
+        torch.backends.cudnn.benchmark = True
+
+    # construct loader
+    t_to_sigma = partial(t_to_sigma_compl, args=args)
+    train_loader, val_loader = construct_loader(args, t_to_sigma)
+
+    model = get_model(args, device, t_to_sigma=t_to_sigma)
+    optimizer, scheduler = get_optimizer_and_scheduler(args, model, scheduler_mode=args.inference_earlystop_goal if args.val_inference_freq is not None else 'min')
+    ema_weights = ExponentialMovingAverage(model.parameters(),decay=args.ema_rate)
+
+    if args.restart_dir:
+        try:
+            dict = torch.load(f'{args.restart_dir}/last_model.pt', map_location=torch.device('cpu'))
+            if args.restart_lr is not None: dict['optimizer']['param_groups'][0]['lr'] = args.restart_lr
+            optimizer.load_state_dict(dict['optimizer'])
+            model.module.load_state_dict(dict['model'], strict=True)
+            if hasattr(args, 'ema_rate'):
+                ema_weights.load_state_dict(dict['ema_weights'], device=device)
+            print("Restarting from epoch", dict['epoch'])
+        except Exception as e:
+            print("Exception", e)
+            dict = torch.load(f'{args.restart_dir}/best_model.pt', map_location=torch.device('cpu'))
+            model.module.load_state_dict(dict, strict=True)
+            print("Due to exception had to take the best epoch and no optimiser")
+
+    numel = sum([p.numel() for p in model.parameters()])
+    print('Model with', numel, 'parameters')
+
+    if args.wandb:
+        wandb.init(
+            entity='entity',
+            settings=wandb.Settings(start_method="fork"),
+            project=args.project,
+            name=args.run_name,
+            config=args
+        )
+        wandb.log({'numel': numel})
+
+    # record parameters
+    run_dir = os.path.join(args.log_dir, args.run_name)
+    yaml_file_name = os.path.join(run_dir, 'model_parameters.yml')
+    save_yaml_file(yaml_file_name, args.__dict__)
+    args.device = device
+
+    train(args, model, optimizer, scheduler, ema_weights, train_loader, val_loader, t_to_sigma, run_dir)
+
+
+if __name__ == '__main__':
+    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+    main_function()

visualizations/README.md

@@ -0,0 +1,14 @@
+## Visualizations of complexes that were unseen during training. EquiBind (cyan), DockDiff highest confidence sample (red), all other DockDiff samples (orange), and the crystal structure (green).
+
+Complex 6agt:
+![Alt Text](example_6agt_symmetric.gif)
+
+Complex 6dz3:
+![Alt Text](example_6dz3_symmetric.gif)
+
+Complex 6gdy:
+![Alt Text](example_6gdy_symmetric.gif)
+
+Complex 6ckl:
+![Alt Text](example_6ckl_symmetric.gif)
+