Notice:
other packages:
import torch
from build_vocab import WordVocab
from pretrain_trfm import TrfmSeq2seq
from utils import split
from transformers import T5EncoderModel, T5Tokenizer
import re
import gc
import numpy as np
import pandas as pd
import pickle
import mathdef smiles_to_vec(Smiles): pad_index = 0 unk_index = 1 eos_index = 2 sos_index = 3 mask_index = 4 vocab = WordVocab.load_vocab('vocab.pkl') def get_inputs(sm): seq_len = 220 sm = sm.split() if len(sm)>218: print('SMILES is too long ({:d})'.format(len(sm))) sm = sm[:109]+sm[-109:] ids = [vocab.stoi.get(token, unk_index) for token in sm] ids = [sos_index] + ids + [eos_index] seg = [1]len(ids) padding = [pad_index](seq_len - len(ids)) ids.extend(padding), seg.extend(padding) return ids, seg def get_array(smiles): x_id, x_seg = [], [] for sm in smiles: a,b = get_inputs(sm) x_id.append(a) x_seg.append(b) return torch.tensor(x_id), torch.tensor(x_seg) trfm = TrfmSeq2seq(len(vocab), 256, len(vocab), 4) trfm.load_state_dict(torch.load('trfm_12_23000.pkl')) trfm.eval() x_split = [split(sm) for sm in Smiles] xid, xseg = get_array(x_split) X = trfm.encode(torch.t(xid)) return X
def Seq_to_vec(Sequence): sequences_Example = [] for i in range(len(Sequence)): zj = '' for j in range(len(Sequence[i]) - 1): zj += Sequence[i][j] + ' ' zj += Sequence[i][-1] sequences_Example.append(zj) tokenizer = T5Tokenizer.from_pretrained("prot_t5_xl_uniref50", do_lower_case=False) model = T5EncoderModel.from_pretrained("prot_t5_xl_uniref50") gc.collect() print(torch.cuda.is_available())
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model = model.to(device)
model = model.eval()
features = []
for i in range(len(sequences_Example)):
print('For sequence ', str(i+1))
sequences_Example_i = sequences_Example[i]
sequences_Example_i = [re.sub(r"[UZOB]", "X", sequences_Example_i)]
ids = tokenizer.batch_encode_plus(sequences_Example_i, add_special_tokens=True, padding=True)
input_ids = torch.tensor(ids['input_ids']).to(device)
attention_mask = torch.tensor(ids['attention_mask']).to(device)
with torch.no_grad():
embedding = model(input_ids=input_ids, attention_mask=attention_mask)
embedding = embedding.last_hidden_state.cpu().numpy()
for seq_num in range(len(embedding)):
seq_len = (attention_mask[seq_num] == 1).sum()
seq_emd = embedding[seq_num][:seq_len - 1]
features.append(seq_emd)
features_normalize = np.zeros([len(features), len(features[0][0])], dtype=float)
for i in range(len(features)):
for k in range(len(features[0][0])):
for j in range(len(features[i])):
features_normalize[i][k] += features[i][j][k]
features_normalize[i][k] /= len(features[i])
return features_normalizeif name == 'main': sequences = ['MEDIPDTSRPPLKYVKGIPLIKYFAEALESLQDFQAQPDDLLISTYPKSGTTWVSEILDMIYQDGDVEKCRRAPVFIRVPFLEFKA' 'PGIPTGLEVLKDTPAPRLIKTHLPLALLPQTLLDQKVKVVYVARNAKDVAVSYYHFYRMAKVHPDPDTWDSFLEKFMAGEVSYGSW' 'YQHVQEWWELSHTHPVLYLFYEDMKENPKREIQKILKFVGRSLPEETVDLIVQHTSFKEMKNNSMANYTTLSPDIMDHSISAFMRK' 'GISGDWKTTFTVAQNERFDADYAKKMEGCGLSFRTQL'] Smiles = ['OC1=CC=C(CC@@HN)C=C1'] seq_vec = Seq_to_vec(sequences) smiles_vec = smiles_to_vec(Smiles) fused_vector = np.concatenate((smiles_vec, seq_vec), axis=1) with open('PreKcat_new/PreKcat_model.pkl', "rb") as f: model = pickle.load(f) Pre_label = model.predict(fused_vector) Pre_label_pow = [math.pow(10, Pre_label[i]) for i in range(len(Pre_label))] print(len(Pre_label)) res = pd.DataFrame({'sequences': sequences, 'Smiles': Smiles, 'Pre_label': Pre_label}) res.to_excel('PreKcat_predicted_label.xlsx')
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