tr2d2-pep/scoring/functions/hemolysis.py

import xgboost as xgb
import torch
import numpy as np
from transformers import AutoModelForMaskedLM
import warnings
import numpy as np
from rdkit import rdBase

rdBase.DisableLog('rdApp.error')
warnings.filterwarnings("ignore", category=DeprecationWarning)
warnings.filterwarnings("ignore", category=UserWarning)
warnings.filterwarnings("ignore", category=FutureWarning)

class Hemolysis:
    
    def __init__(self, tokenizer, base_path, device=None, emb_model=None):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if device is None else device
        self.predictor = xgb.Booster(model_file=f'{base_path}/TR2-D2/tr2d2-pep/scoring/functions/classifiers/hemolysis-xgboost.json')
        self.emb_model = emb_model if emb_model is not None else AutoModelForMaskedLM.from_pretrained('aaronfeller/PeptideCLM-23M-all').roformer.to(device).eval()
        self.tokenizer = tokenizer
    
    def generate_embeddings(self, sequences):
        embeddings = []
        for sequence in sequences:
            tokenized = self.tokenizer(sequence, return_tensors='pt')
            tokenized = {k: v.to(self.device) for k, v in tokenized.items()}
            with torch.no_grad():
                output = self.emb_model(**tokenized)
            # Mean pooling across sequence length
            embedding = output.last_hidden_state.mean(dim=1).squeeze(0).cpu().numpy()
            embeddings.append(embedding)
        return np.array(embeddings)
    
    def get_scores(self, input_seqs: list):
        scores = np.ones(len(input_seqs))
        features = self.generate_embeddings(input_seqs)
        
        if len(features) == 0:
            return scores
        
        features = np.nan_to_num(features, nan=0.)
        features = np.clip(features, np.finfo(np.float32).min, np.finfo(np.float32).max)
        
        features = xgb.DMatrix(features)
        
        probs = self.predictor.predict(features)
        # return the probability of it being not hemolytic
        return scores - probs
    
    def __call__(self, input_seqs: list):
        scores = self.get_scores(input_seqs)
        return scores
    
def unittest():
    hemo = Hemolysis()
    seq = ["[te]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"]
    print(hemo.tokenizer.vocab_size)
    scores = hemo(input_seqs=seq)
    print(scores)


if __name__ == '__main__':
    unittest()