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import os
import pandas as pd
import torch
from torch.nn import functional as F
from transformers import AutoTokenizer
from rdkit import Chem
from .util.utils import *
from tqdm import tqdm
from .train import markerModel
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = '0 '
device_count = torch.cuda.device_count()
device_biberta= torch.device('cuda' if torch.cuda.is_available() else "cpu")
device = torch.device('cpu')
a_model_name = 'DeepChem/ChemBERTa-10M-MLM'
d_model_name = 'DeepChem/ChemBERTa-10M-MTR'
tokenizer = AutoTokenizer.from_pretrained(a_model_name)
d_tokenizer = AutoTokenizer.from_pretrained(d_model_name)
#--bibertaModel
##-- hyper param config file Load --##
config = load_hparams('tool/dap/config/predict.json')
config = DictX(config)
model = markerModel(config.d_model_name, config.p_model_name,
config.lr, config.dropout, config.layer_features, config.loss_fn, config.layer_limit, config.pretrained['chem'], config.pretrained['prot'])
model = markerModel.load_from_checkpoint(config.load_checkpoint,strict=False)
model.eval()
model.freeze()
if device_biberta.type == 'cuda':
model = torch.nn.DataParallel(model)
def get_biberta(drug_inputs, prot_inputs):
output_preds = model(drug_inputs, prot_inputs)
predict = torch.squeeze( (output_preds)).tolist()
# output_preds = torch.relu(output_preds)
# predict = torch.tanh(output_preds)
# predict = predict.squeeze(dim=1).tolist()
return predict
def biberta_prediction(smiles, aas):
try:
aas_input = []
for ass_data in aas:
aas_input.append(' '.join(list(ass_data)))
a_inputs = tokenizer(smiles, padding='max_length', max_length=510, truncation=True, return_tensors="pt")
# d_inputs = tokenizer(smiles, truncation=True, return_tensors="pt")
a_input_ids = a_inputs['input_ids'].to(device)
a_attention_mask = a_inputs['attention_mask'].to(device)
a_inputs = {'input_ids': a_input_ids, 'attention_mask': a_attention_mask}
d_inputs = d_tokenizer(aas_input, padding='max_length', max_length=510, truncation=True, return_tensors="pt")
# p_inputs = prot_tokenizer(aas_input, truncation=True, return_tensors="pt")
d_input_ids = d_inputs['input_ids'].to(device)
d_attention_mask = d_inputs['attention_mask'].to(device)
d_inputs = {'input_ids': d_input_ids, 'attention_mask': d_attention_mask}
output_predict = get_biberta(a_inputs, d_inputs)
output_list = [{'acceptor': smiles[i], 'donor': aas[i], 'predict': output_predict[i]} for i in range(0,len(aas))]
return output_list
except Exception as e:
print(e)
return {'Error_message': e}
def smiles_aas_test(file):
batch_se = 80
try:
datas = []
biberta_list = []
biberta_datas = []
smiles_aas = pd.read_csv(file)
smiles_d , smiles_a = (smiles_aas['donor'],smiles_aas['acceptor'])
donor,acceptor =[],[]
for i in smiles_d:
s = Chem.MolToSmiles(Chem.MolFromSmiles(i))
donor.append(s)
for i in smiles_a:
s = Chem.MolToSmiles(Chem.MolFromSmiles(i))
acceptor.append(s)
output_pred = biberta_prediction(list(acceptor), list(donor) )
if len(datas) == 0:
datas = output_pred
else:
datas = datas + output_pred
# ## -- Export result data to csv -- ##
df = pd.DataFrame(datas)
# print(df)
return datas
except Exception as e:
print(e)
return {'Error_message': e}
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