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import os
import pickle
import numpy as np
import torch
from torch_geometric.data import Batch
from torch_geometric.transforms import Compose
from tqdm.auto import tqdm
import onescience.utils.targetdiff.misc as misc
import onescience.utils.targetdiff.transforms as trans
from onescience.datapipes.targetdiff import get_dataset
from onescience.datapipes.targetdiff.pl_data import FOLLOW_BATCH
from models.molopt_score_model import ScorePosNet3D
def data_likelihood_estimation(model, data, time_steps, batch_size=1, device='cuda:0'):
num_timesteps = len(time_steps)
num_batch = int(np.ceil(num_timesteps / batch_size))
all_kl_pos, all_kl_v = [], []
cur_i = 0
# t in [T-1, ..., 0]
for i in range(num_batch):
n_data = batch_size if i < num_batch - 1 else num_timesteps - batch_size * (num_batch - 1)
batch = Batch.from_data_list([data.clone() for _ in range(n_data)], follow_batch=FOLLOW_BATCH).to(device)
time_step = time_steps[cur_i:cur_i + n_data]
kl_pos, kl_v = model.likelihood_estimation(
protein_pos=batch.protein_pos,
protein_v=batch.protein_atom_feature.float(),
batch_protein=batch.protein_element_batch,
ligand_pos=batch.ligand_pos,
ligand_v=batch.ligand_atom_feature_full,
batch_ligand=batch.ligand_element_batch,
time_step=time_step
)
all_kl_pos.append(kl_pos)
all_kl_v.append(kl_v)
cur_i += n_data
# prior
batch = Batch.from_data_list([data.clone() for _ in range(1)], follow_batch=FOLLOW_BATCH).to(device)
time_step = torch.tensor([model.num_timesteps], device=device)
kl_pos_prior, kl_v_prior = model.likelihood_estimation(
protein_pos=batch.protein_pos,
protein_v=batch.protein_atom_feature.float(),
batch_protein=batch.protein_element_batch,
ligand_pos=batch.ligand_pos,
ligand_v=batch.ligand_atom_feature_full,
batch_ligand=batch.ligand_element_batch,
time_step=time_step
)
all_kl_pos, all_kl_v = torch.cat(all_kl_pos), torch.cat(all_kl_v)
sum_kl_pos, sum_kl_v = model.num_timesteps * torch.mean(all_kl_pos), model.num_timesteps * torch.mean(all_kl_v)
all_kl_pos, all_kl_v = torch.cat([all_kl_pos, kl_pos_prior]), torch.cat([all_kl_v, kl_v_prior])
sum_kl_pos += kl_pos_prior[0]
sum_kl_v += kl_v_prior[0]
return all_kl_pos.cpu(), all_kl_v.cpu(), sum_kl_pos.item(), sum_kl_v.item()
def get_dataset_result(dset, affinity_info):
valid_id = []
for data_id in tqdm(range(len(dset)), desc='Filtering data'):
data = dset[data_id]
ligand_fn_key = data.ligand_filename[:-4]
pk = affinity_info[ligand_fn_key]['pk']
if pk > 0:
valid_id.append(data_id)
print(f'There are {len(valid_id)} examples with valid pK in total.')
all_results = []
for data_id in tqdm(valid_id, desc='Evaluating'):
data = dset[data_id]
# likelihoods
time_steps = torch.tensor(list(range(0, 1000, 100)), device=args.device)
all_kl_pos, all_kl_v, sum_kl_pos, sum_kl_v = data_likelihood_estimation(
model, data, time_steps, batch_size=args.batch_size, device=args.device)
kl = sum_kl_pos + sum_kl_v
# embedding
batch = Batch.from_data_list([data.clone() for _ in range(1)], follow_batch=FOLLOW_BATCH).to(args.device)
preds = model.fetch_embedding(
protein_pos=batch.protein_pos,
protein_v=batch.protein_atom_feature.float(),
batch_protein=batch.protein_element_batch,
ligand_pos=batch.ligand_pos,
ligand_v=batch.ligand_atom_feature_full,
batch_ligand=batch.ligand_element_batch,
)
# gather results
ligand_fn_key = data.ligand_filename[:-4]
result = {
'idx': data_id,
**affinity_info[ligand_fn_key],
'kl_pos': all_kl_pos,
'kl_v': all_kl_v,
'nll': kl,
'pred_ligand_v': preds['pred_ligand_v'].cpu(),
'final_h': preds['final_h'].cpu(),
'final_ligand_h': preds['final_ligand_h'].cpu()
}
all_results.append(result)
return all_results
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=str, default='cuda:0')
parser.add_argument('--config', type=str, default='configs/sampling/final_diffusion/aromatic_176k.yml')
parser.add_argument('--affinity_path', type=str, default='data/affinity_info.pkl')
parser.add_argument('--index_path', type=str, default='data/crossdocked_v1.1_rmsd1.0_pocket10/index.pkl')
parser.add_argument('--batch_size', type=int, default=4)
parser.add_argument('--result_path', type=str, default='./outputs_embedding')
args = parser.parse_args()
logger = misc.get_logger('evaluate')
if os.path.exists(args.affinity_path):
with open(args.affinity_path, 'rb') as f:
affinity_info = pickle.load(f)
else:
# collect index
with open(args.index_path, 'rb') as f:
index = pickle.load(f)
affinity_info = {}
for pdb_file, sdf_file, rmsd in index:
affinity_info[sdf_file[:-4]] = {'rmsd': rmsd}
# fetch reference vina score / binding affinity
types_path = 'data/CrossDocked2020/types/it2_tt_v1.1_completeset_train0.types'
with open(types_path, 'r') as f:
for ln in tqdm(f.readlines()):
# <label> <pK> <RMSD to crystal> <Receptor> <Ligand> # <Autodock Vina score>
_, pk, rmsd, protein_fn, ligand_fn, vina = ln.split()
ligand_raw_fn = ligand_fn[:ligand_fn.rfind('.')]
if ligand_raw_fn in affinity_info:
affinity_info[ligand_raw_fn].update({
'pk': float(pk),
'vina': float(vina[1:])
})
# save affinity info
with open(args.affinity_path, 'wb') as f:
pickle.dump(affinity_info, f)
# Load config
config = misc.load_config(args.config)
logger.info(config)
misc.seed_all(config.sample.seed)
# Load checkpoint
ckpt = torch.load(config.model.checkpoint, map_location=args.device)
# Transforms
protein_featurizer = trans.FeaturizeProteinAtom()
ligand_featurizer = trans.FeaturizeLigandAtom(ckpt['config'].data.transform.ligand_atom_mode)
transform_list = [
protein_featurizer,
ligand_featurizer,
trans.FeaturizeLigandBond(),
]
if ckpt['config'].data.transform.random_rot:
transform_list.append(trans.RandomRotation())
transform = Compose(transform_list)
# Load dataset
dataset, subsets = get_dataset(
config=ckpt['config'].data,
transform=transform
)
train_set, test_set = subsets['train'], subsets['test']
logger.info(f'Successfully load the dataset (size: {len(test_set)})!')
# Load model
model = ScorePosNet3D(
ckpt['config'].model,
protein_atom_feature_dim=protein_featurizer.feature_dim,
ligand_atom_feature_dim=ligand_featurizer.feature_dim
).to(args.device)
model.load_state_dict(ckpt['model'], strict=False if 'train_config' in config.model else True)
logger.info(f'Successfully load the model! {config.model.checkpoint}')
# filter data with valid pK, compute likelihood and embedding
valid_train_results = get_dataset_result(train_set, affinity_info)
valid_test_results = get_dataset_result(test_set, affinity_info)
os.makedirs(args.result_path, exist_ok=True)
torch.save(valid_train_results, os.path.join(args.result_path, 'crossdocked_train.pt'))
torch.save(valid_test_results, os.path.join(args.result_path, 'crossdocked_test.pt'))
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