feat: inference.py script

This is for running single data points through our model.

inference.py

@@ -0,0 +1,84 @@
+# %%
+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
+
+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
+
+lig_seq = "CN(C)CC=CC(=O)NC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C=C3)F)Cl)OC4CCOC4"
+
+#%% 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)
+
+#%% 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'
+
+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)
+
+
+
+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)
+
+#%% Loading the model
+from src.utils.loader import Loader
+from src import TUNED_MODEL_CONFIGS
+import os
+
+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']
+    }
+
+
+model_kwargs = reformat_kwargs(TUNED_MODEL_CONFIGS['davis_esm'])
+
+MODEL_KEY = Loader.get_model_key(**model_kwargs)
+
+model_p_tmp = f'{cfg.MODEL_SAVE_DIR}/{MODEL_KEY}.model_tmp'
+model_p = f'{cfg.MODEL_SAVE_DIR}/{MODEL_KEY}.model'
+
+# 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}"
+
+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'])
+
+