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| # %% | ||
| from src.data_prep.feature_extraction.ligand import smile_to_graph | ||
| from src.data_prep.feature_extraction.protein import target_to_graph | ||
| from src.data_prep.feature_extraction.protein_edges import get_target_edge_weights | ||
| from src import cfg | ||
| import torch | ||
| import torch_geometric as torchg | ||
| import numpy as np | ||
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| DATA = cfg.DATA_OPT.davis | ||
| lig_feature = cfg.LIG_FEAT_OPT.original | ||
| lig_edge = cfg.LIG_EDGE_OPT.binary | ||
| pro_feature = cfg.PRO_FEAT_OPT.nomsa | ||
| pro_edge = cfg.PRO_EDGE_OPT.binary | ||
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| lig_seq = "CN(C)CC=CC(=O)NC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C=C3)F)Cl)OC4CCOC4" | ||
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| #%% build ligand graph | ||
| mol_feat, mol_edge = smile_to_graph(lig_seq, lig_feature=lig_feature, lig_edge=lig_edge) | ||
| lig = torchg.data.Data(x=torch.Tensor(mol_feat), edge_index=torch.LongTensor(mol_edge), | ||
| lig_seq=lig_seq) | ||
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| #%% build protein graph | ||
| # predicted using - https://zhanggroup.org/NeBcon/ | ||
| prot_id = 'EGFR(L858R)' | ||
| pro_seq = 'MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA' | ||
| cmap_p = f'/cluster/home/t122995uhn/projects/data/davis/pconsc4/{prot_id}.npy' | ||
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| pro_cmap = np.load(cmap_p) | ||
| # updated_seq is for updated foldseek 3di combined seq | ||
| updated_seq, extra_feat, edge_idx = target_to_graph(target_sequence=pro_seq, | ||
| contact_map=pro_cmap, | ||
| threshold=8.0 if DATA is cfg.DATA_OPT.PDBbind else -0.5, | ||
| pro_feat=pro_feature) | ||
| pro_feat = torch.Tensor(extra_feat) | ||
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| pro = torchg.data.Data(x=torch.Tensor(pro_feat), | ||
| edge_index=torch.LongTensor(edge_idx), | ||
| pro_seq=updated_seq, # Protein sequence for downstream esm model | ||
| prot_id=prot_id, | ||
| edge_weight=None) | ||
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| #%% Loading the model | ||
| from src.utils.loader import Loader | ||
| from src import TUNED_MODEL_CONFIGS | ||
| import os | ||
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| def reformat_kwargs(model_kwargs): | ||
| return { | ||
| 'model': model_kwargs['model'], | ||
| 'data': model_kwargs['dataset'], | ||
| 'pro_feature': model_kwargs['feature_opt'], | ||
| 'edge': model_kwargs['edge_opt'], | ||
| 'batch_size': model_kwargs['batch_size'], | ||
| 'lr': model_kwargs['lr'], | ||
| 'dropout': model_kwargs['architecture_kwargs']['dropout'], | ||
| 'n_epochs': model_kwargs.get('n_epochs', 2000), # Assuming a default value for n_epochs | ||
| 'pro_overlap': model_kwargs.get('pro_overlap', False), # Assuming a default or None | ||
| 'fold': model_kwargs.get('fold', 0), # Assuming a default or None | ||
| 'ligand_feature': model_kwargs['lig_feat_opt'], | ||
| 'ligand_edge': model_kwargs['lig_edge_opt'] | ||
| } | ||
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| model_kwargs = reformat_kwargs(TUNED_MODEL_CONFIGS['davis_esm']) | ||
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| MODEL_KEY = Loader.get_model_key(**model_kwargs) | ||
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| model_p_tmp = f'{cfg.MODEL_SAVE_DIR}/{MODEL_KEY}.model_tmp' | ||
| model_p = f'{cfg.MODEL_SAVE_DIR}/{MODEL_KEY}.model' | ||
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| # MODEL_KEY = 'DDP-' + MODEL_KEY # distributed model | ||
| model_p = model_p if os.path.isfile(model_p) else model_p_tmp | ||
| assert os.path.isfile(model_p), f"MISSING MODEL CHECKPOINT {model_p}" | ||
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| print(model_p) | ||
| # %% | ||
| args = model_kwargs | ||
| model = Loader.init_model(model=args['model'], pro_feature=args['pro_feature'], | ||
| pro_edge=args['edge'], **args['architecture_kwargs']) | ||
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