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import sys |
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import os |
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sys.path.append('/home/st512/peptune/scripts/peptide-mdlm-mcts') |
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import xgboost as xgb |
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import torch |
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import numpy as np |
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from transformers import AutoModelForMaskedLM |
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from tokenizer.my_tokenizers import SMILES_SPE_Tokenizer |
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import warnings |
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import numpy as np |
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from rdkit.Chem import Descriptors, rdMolDescriptors |
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from rdkit import Chem, rdBase, DataStructs |
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from rdkit.Chem import AllChem |
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from typing import List |
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from scoring.functions.transformation import TransformFunction |
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from transformers import AutoModelForMaskedLM |
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rdBase.DisableLog('rdApp.error') |
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warnings.filterwarnings("ignore", category=DeprecationWarning) |
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warnings.filterwarnings("ignore", category=UserWarning) |
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warnings.filterwarnings("ignore", category=FutureWarning) |
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class Solubility: |
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def __init__(self): |
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self.predictor = xgb.Booster(model_file='/home/st512/peptune/scripts/peptide-mdlm-mcts/scoring/functions/solubility/new_train/best_model.json') |
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self.emb_model = AutoModelForMaskedLM.from_pretrained('aaronfeller/PeptideCLM-23M-all').roformer |
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self.tokenizer = SMILES_SPE_Tokenizer('/home/st512/peptune/scripts/peptide-mdlm-mcts/tokenizer/new_vocab.txt', |
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'/home/st512/peptune/scripts/peptide-mdlm-mcts/tokenizer/new_splits.txt') |
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def generate_embeddings(self, sequences): |
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embeddings = [] |
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for sequence in sequences: |
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tokenized = self.tokenizer(sequence, return_tensors='pt') |
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with torch.no_grad(): |
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output = self.emb_model(**tokenized) |
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embedding = output.last_hidden_state.mean(dim=1).squeeze(0).cpu().numpy() |
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embeddings.append(embedding) |
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return np.array(embeddings) |
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def get_scores(self, input_seqs: list): |
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scores = np.zeros(len(input_seqs)) |
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features = self.generate_embeddings(input_seqs) |
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if len(features) == 0: |
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return scores |
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features = np.nan_to_num(features, nan=0.) |
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features = np.clip(features, np.finfo(np.float32).min, np.finfo(np.float32).max) |
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features = xgb.DMatrix(features) |
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scores = self.predictor.predict(features) |
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return scores |
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def __call__(self, input_seqs: list): |
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scores = self.get_scores(input_seqs) |
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return scores |
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def unittest(): |
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solubility = Solubility() |
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seq = ["NCC(=O)N[C@H](CS)C(=O)N[C@@H](CO)C(=O)NCC(=O)N[C@@H](CC1=CN=C-N1)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CC(=CN2)C1=C2C=CC=C1)C(=O)N[C@@H](c1ccc(cc1)F)C(=O)N[C@@H]([C@H](CC)C)C(=O)N[C@@H](CCCO)C(=O)N[C@@H](CC1=CN=C-N1)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CO)C(=O)O"] |
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scores = solubility(input_seqs=seq) |
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print(scores) |
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if __name__ == '__main__': |
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unittest() |
