train.py

from __future__ import annotations
import time
import json 
import gradio as gr
from gradio_molecule3d import Molecule3D
import torch
from pinder.core import get_pinder_location
get_pinder_location()
from pytorch_lightning import LightningModule

import torch
import lightning.pytorch as pl
import torch.nn.functional as F

import torch.nn as nn
import torchmetrics
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import MessagePassing
from torch_geometric.nn import global_mean_pool
from torch.nn import Sequential, Linear, BatchNorm1d, ReLU
from torch_scatter import scatter
from torch.nn import Module


import pinder.core as pinder
pinder.__version__
from torch_geometric.loader import DataLoader
from pinder.core.loader.dataset import get_geo_loader
from pinder.core import download_dataset
from pinder.core import get_index
from pinder.core import get_metadata
from pathlib import Path
import pandas as pd
from pinder.core import PinderSystem
import torch
from pinder.core.loader.dataset import PPIDataset
from pinder.core.loader.geodata import NodeRepresentation
import pickle
from pinder.core import get_index, PinderSystem
from torch_geometric.data import HeteroData
import os

from enum import Enum

import numpy as np
import torch
import lightning.pytorch as pl
from numpy.typing import NDArray
from torch_geometric.data import HeteroData

from pinder.core.index.system import PinderSystem
from pinder.core.loader.structure import Structure
from pinder.core.utils import constants as pc
from pinder.core.utils.log import setup_logger
from pinder.core.index.system import _align_monomers_with_mask
from pinder.core.loader.structure import Structure

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import MessagePassing
from torch_geometric.nn import global_mean_pool
from torch.nn import Sequential, Linear, BatchNorm1d, ReLU
from torch_scatter import scatter
from torch.nn import Module
import time
from torch_geometric.nn import global_max_pool
import copy
import inspect
import warnings
from typing import Optional, Tuple, Union

import torch
from torch import Tensor

from torch_geometric.data import Data, Dataset, HeteroData
from torch_geometric.data.feature_store import FeatureStore
from torch_geometric.data.graph_store import GraphStore
from torch_geometric.loader import (
    LinkLoader,
    LinkNeighborLoader,
    NeighborLoader,
    NodeLoader,
)
from torch_geometric.loader.dataloader import DataLoader
from torch_geometric.loader.utils import get_edge_label_index, get_input_nodes
from torch_geometric.sampler import BaseSampler, NeighborSampler
from torch_geometric.typing import InputEdges, InputNodes

try:
    from lightning.pytorch import LightningDataModule as PLLightningDataModule
    no_pytorch_lightning = False
except (ImportError, ModuleNotFoundError):
    PLLightningDataModule = object
    no_pytorch_lightning = True

from lightning.pytorch.callbacks import ModelCheckpoint
from lightning.pytorch.loggers.tensorboard import TensorBoardLogger
from lightning.pytorch.callbacks.early_stopping import EarlyStopping
from torch_geometric.data.lightning.datamodule import LightningDataset
from pytorch_lightning.loggers.wandb import WandbLogger
def get_system(system_id: str) -> PinderSystem:
    return PinderSystem(system_id)
from Bio import PDB
from Bio.PDB.PDBIO import PDBIO


# To create dataset, we have used only PINDER datyaset with following steps as follows:

log = setup_logger(__name__)

try:
    from torch_cluster import knn_graph

    torch_cluster_installed = True
except ImportError as e:
    log.warning(
        "torch-cluster is not installed!"
        "Please install the appropriate library for your pytorch installation."
        "See https://github.com/rusty1s/pytorch_cluster/issues/185 for background."
    )
    torch_cluster_installed = False


def structure2tensor(
    atom_coordinates: NDArray[np.double] | None = None,
    atom_types: NDArray[np.str_] | None = None,
    element_types: NDArray[np.str_] | None = None,
    residue_coordinates: NDArray[np.double] | None = None,
    residue_ids: NDArray[np.int_] | None = None,
    residue_types: NDArray[np.str_] | None = None,
    chain_ids: NDArray[np.str_] | None = None,
    dtype: torch.dtype = torch.float32,
) -> dict[str, torch.Tensor]:
    property_dict = {}
    if atom_types is not None:
        unknown_name_idx = max(pc.ALL_ATOM_POSNS.values()) + 1
        types_array_at = np.zeros((len(atom_types), 1))
        for i, name in enumerate(atom_types):
            types_array_at[i] = pc.ALL_ATOM_POSNS.get(name, unknown_name_idx)
        property_dict["atom_types"] = torch.tensor(types_array_at).type(dtype)
    if element_types is not None:
        types_array_ele = np.zeros((len(element_types), 1))
        for i, name in enumerate(element_types):
            types_array_ele[i] = pc.ELE2NUM.get(name, pc.ELE2NUM["other"])
        property_dict["element_types"] = torch.tensor(types_array_ele).type(dtype)
    if residue_types is not None:
        unknown_name_idx = max(pc.AA_TO_INDEX.values()) + 1
        types_array_res = np.zeros((len(residue_types), 1))
        for i, name in enumerate(residue_types):
            types_array_res[i] = pc.AA_TO_INDEX.get(name, unknown_name_idx)
        property_dict["residue_types"] = torch.tensor(types_array_res).type(dtype)

    if atom_coordinates is not None:
        property_dict["atom_coordinates"] = torch.tensor(atom_coordinates, dtype=dtype)
        
    if residue_coordinates is not None:
        property_dict["residue_coordinates"] = torch.tensor(
            residue_coordinates, dtype=dtype
        )
    if residue_ids is not None:
        property_dict["residue_ids"] = torch.tensor(residue_ids, dtype=dtype)
    if chain_ids is not None:
        property_dict["chain_ids"] = torch.zeros(len(chain_ids), dtype=dtype)
        property_dict["chain_ids"][chain_ids == "L"] = 1
    return property_dict


class NodeRepresentation(Enum):
    Surface = "surface"
    Atom = "atom"
    Residue = "residue"


class PairedPDB(HeteroData):  # type: ignore
    @classmethod
    def from_tuple_system(
        cls,
    
        tupal: tuple = (Structure , Structure , Structure),
        
        add_edges: bool = True,
        k: int = 10,
        
    ) -> PairedPDB:
        return cls.from_structure_pair(
            
            holo=tupal[0],
            apo=tupal[1],
            add_edges=add_edges,
            k=k,
        )

    @classmethod
    def from_structure_pair(
        cls,

        holo: Structure,
        apo: Structure,
        
        add_edges: bool = True,
        k: int = 10,
    ) -> PairedPDB:
        graph = cls()
        holo_calpha = holo.filter("atom_name", mask=["CA"])
        apo_calpha = apo.filter("atom_name", mask=["CA"])
        r_h = (holo.dataframe['chain_id'] == 'R').sum()
        r_a = (apo.dataframe['chain_id'] == 'R').sum()
        
        holo_r_props = structure2tensor(
            atom_coordinates=holo.coords[:r_h],
            atom_types=holo.atom_array.atom_name[:r_h],
            element_types=holo.atom_array.element[:r_h],
            residue_coordinates=holo_calpha.coords[:r_h],
            residue_types=holo_calpha.atom_array.res_name[:r_h],
            residue_ids=holo_calpha.atom_array.res_id[:r_h],
        )
        holo_l_props = structure2tensor(
            atom_coordinates=holo.coords[r_h:],
            
            atom_types=holo.atom_array.atom_name[r_h:],
            element_types=holo.atom_array.element[r_h:],
            residue_coordinates=holo_calpha.coords[r_h:],
            residue_types=holo_calpha.atom_array.res_name[r_h:],
            residue_ids=holo_calpha.atom_array.res_id[r_h:],
        )
        apo_r_props = structure2tensor(
            atom_coordinates=apo.coords[:r_a],
            atom_types=apo.atom_array.atom_name[:r_a],
            element_types=apo.atom_array.element[:r_a],
            residue_coordinates=apo_calpha.coords[:r_a],
            residue_types=apo_calpha.atom_array.res_name[:r_a],
            residue_ids=apo_calpha.atom_array.res_id[:r_a],
        )
        apo_l_props = structure2tensor(
            atom_coordinates=apo.coords[r_a:],
            atom_types=apo.atom_array.atom_name[r_a:],
            element_types=apo.atom_array.element[r_a:],
            residue_coordinates=apo_calpha.coords[r_a:],
            residue_types=apo_calpha.atom_array.res_name[r_a:],
            residue_ids=apo_calpha.atom_array.res_id[r_a:],
        )
       
        
       
        graph["ligand"].x = apo_l_props["atom_types"]
        graph["ligand"].pos = apo_l_props["atom_coordinates"]
        graph["receptor"].x = apo_r_props["atom_types"]
        graph["receptor"].pos = apo_r_props["atom_coordinates"]
        graph["ligand"].y = holo_l_props["atom_coordinates"]
        # graph["ligand"].pos = holo_l_props["atom_coordinates"]
        graph["receptor"].y = holo_r_props["atom_coordinates"]
        # graph["receptor"].pos = holo_r_props["atom_coordinates"]
        if add_edges and torch_cluster_installed:
                graph["ligand"].edge_index = knn_graph(
                    graph["ligand"].pos, k=k
                )
                graph["receptor"].edge_index = knn_graph(
                    graph["receptor"].pos, k=k
                )
                # graph["ligand"].edge_index = knn_graph(
                #     graph["ligand"].pos, k=k
                # )
                # graph["receptor"].edge_index = knn_graph(
                #     graph["receptor"].pos, k=k
                # )

        return graph
    
index = get_index()
train = index[index.split == "train"].copy()
val = index[index.split == "val"].copy()
test = index[index.split == "test"].copy()   
train_filtered = train[(train['apo_R'] == True) & (train['apo_L'] == True)].copy()
val_filtered = val[(val['apo_R'] == True) & (val['apo_L'] == True)].copy()
test_filtered = test[(test['apo_R'] == True) & (test['apo_L'] == True)].copy()

train_apo = [get_system(train_filtered.id.iloc[i]).create_masked_bound_unbound_complexes(
    monomer_types=["apo"], renumber_residues=True
) for i in range(0, 10000)]

train_new_apo11 = [get_system(train_filtered.id.iloc[i]).create_masked_bound_unbound_complexes(
    monomer_types=["apo"], renumber_residues=True
) for i in range(10000,10908)]

train_new_apo12 = [get_system(train_filtered.id.iloc[i]).create_masked_bound_unbound_complexes(
#     monomer_types=["apo"], renumber_residues=True
) for i in range(10908,11816)]

val_new_apo1 = [get_system(val_filtered.id.iloc[i]).create_masked_bound_unbound_complexes(
    monomer_types=["apo"], renumber_residues=True
) for i in range(0,342)]

test_new_apo1 = [get_system(test_filtered.id.iloc[i]).create_masked_bound_unbound_complexes(
    monomer_types=["apo"], renumber_residues=True
) for i in range(0,342)]

val_apo = val_new_apo1 + train_new_apo11
test_apo = test_new_apo1 + train_new_apo12

import pickle
# with open("train_apo.pkl", "wb") as file:
#     pickle.dump(train_apo, file)
    
# with open("val_apo.pkl", "wb") as file:
#     pickle.dump(val_apo, file)
    
# with open("test_apo.pkl", "wb") as file:
#     pickle.dump(test_apo, file)
# with open("train_apo.pkl", "rb") as file:
#     train_apo = pickle.load(file)
    
# with open("val_apo.pkl", "rb") as file:
#     val_apo = pickle.load(file)
    
# with open("test_apo.pkl", "rb") as file:
#     test_apo = pickle.load(file)
    
# # %%
train_geo = [PairedPDB.from_tuple_system(train_apo[i]) for i in range(0,len(train_apo))]
val_geo = [PairedPDB.from_tuple_system(val_apo[i]) for i in range(0,len(val_apo))]
test_geo = [PairedPDB.from_tuple_system(test_apo[i]) for i in range(0,len(test_apo))]
# # %%
# Train= []
# for i in range(0,len(train_geo)):
#     data = HeteroData()
#     data["ligand"].x = train_geo[i]["ligand"].x
#     data['ligand'].y = train_geo[i]["ligand"].y
#     data["ligand"].pos = train_geo[i]["ligand"].pos
#     data["ligand","ligand"].edge_index = train_geo[i]["ligand"]
#     data["receptor"].x = train_geo[i]["receptor"].x
#     data['receptor'].y = train_geo[i]["receptor"].y
#     data["receptor"].pos = train_geo[i]["receptor"].pos
#     data["receptor","receptor"].edge_index = train_geo[i]["receptor"]
#     #torch.save(data, f"./data/processed/train_sample_{i}.pt")
#     Train.append(data)
    
from torch_geometric.data import HeteroData
import torch_sparse
from torch_geometric.edge_index import to_sparse_tensor
import torch

# Example of converting edge indices to SparseTensor and storing them in HeteroData

Train1 = []
for i in range(len(train_geo)):
    data = HeteroData()
    # Define ligand node features
    data["ligand"].x = train_geo[i]["ligand"].x
    data["ligand"].y = train_geo[i]["ligand"].y
    data["ligand"].pos = train_geo[i]["ligand"].pos
    # Convert ligand edge index to SparseTensor
    ligand_edge_index = train_geo[i]["ligand"]["edge_index"]
    data["ligand", "ligand"].edge_index = to_sparse_tensor(ligand_edge_index, sparse_sizes=(train_geo[i]["ligand"].num_nodes,)*2)

    # Define receptor node features
    data["receptor"].x = train_geo[i]["receptor"].x
    data["receptor"].y = train_geo[i]["receptor"].y
    data["receptor"].pos = train_geo[i]["receptor"].pos
    # Convert receptor edge index to SparseTensor
    receptor_edge_index = train_geo[i]["receptor"]["edge_index"]
    data["receptor", "receptor"].edge_index = to_sparse_tensor(receptor_edge_index, sparse_sizes=(train_geo[i]["receptor"].num_nodes,)*2)

    Train1.append(data)


# # %%
# Val= []
# for i in range(0,len(val_geo)):
#     data = HeteroData()
#     data["ligand"].x = val_geo[i]["ligand"].x
#     data['ligand'].y = val_geo[i]["ligand"].y
#     data["ligand"].pos = val_geo[i]["ligand"].pos
#     data["ligand","ligand"].edge_index = val_geo[i]["ligand"]
#     data["receptor"].x = val_geo[i]["receptor"].x
#     data['receptor'].y = val_geo[i]["receptor"].y
#     data["receptor"].pos = val_geo[i]["receptor"].pos
#     data["receptor","receptor"].edge_index = val_geo[i]["receptor"]
#     #torch.save(data, f"./data/processed/val_sample_{i}.pt")
#     Val.append(data)
Val1 = []
for i in range(len(val_geo)):
    data = HeteroData()
    # Define ligand node features
    data["ligand"].x = val_geo[i]["ligand"].x
    data["ligand"].y = val_geo[i]["ligand"].y
    data["ligand"].pos = val_geo[i]["ligand"].pos
    # Convert ligand edge index to SparseTensor
    ligand_edge_index = val_geo[i]["ligand"]["edge_index"]
    data["ligand", "ligand"].edge_index = to_sparse_tensor(ligand_edge_index, sparse_sizes=(val_geo[i]["ligand"].num_nodes,)*2)

    # Define receptor node features
    data["receptor"].x = val_geo[i]["receptor"].x
    data["receptor"].y = val_geo[i]["receptor"].y
    data["receptor"].pos = val_geo[i]["receptor"].pos
    # Convert receptor edge index to SparseTensor
    receptor_edge_index = val_geo[i]["receptor"]["edge_index"]
    data["receptor", "receptor"].edge_index = to_sparse_tensor(receptor_edge_index, sparse_sizes=(val_geo[i]["receptor"].num_nodes,)*2)

    Val1.append(data)
# # %%
# Test= []
# for i in range(0,len(test_geo)):
#     data = HeteroData()
#     data["ligand"].x = test_geo[i]["ligand"].x
#     data['ligand'].y = test_geo[i]["ligand"].y
#     data["ligand"].pos = test_geo[i]["ligand"].pos
#     data["ligand","ligand"].edge_index = test_geo[i]["ligand"]
#     data["receptor"].x = test_geo[i]["receptor"].x
#     data['receptor'].y = test_geo[i]["receptor"].y
#     data["receptor"].pos = test_geo[i]["receptor"].pos
#     data["receptor","receptor"].edge_index = test_geo[i]["receptor"]
#     #torch.save(data, f"./data/processed/test_sample_{i}.pt")
#     Test.append(data)
Test1 = []
for i in range(len(test_geo)):
    data = HeteroData()
    # Define ligand node features
    data["ligand"].x = test_geo[i]["ligand"].x
    data["ligand"].y = test_geo[i]["ligand"].y
    data["ligand"].pos = test_geo[i]["ligand"].pos
    # Convert ligand edge index to SparseTensor
    ligand_edge_index = test_geo[i]["ligand"]["edge_index"]
    data["ligand", "ligand"].edge_index = to_sparse_tensor(ligand_edge_index, sparse_sizes=(test_geo[i]["ligand"].num_nodes,)*2)

    # Define receptor node features
    data["receptor"].x = test_geo[i]["receptor"].x
    data["receptor"].y = test_geo[i]["receptor"].y
    data["receptor"].pos = test_geo[i]["receptor"].pos
    # Convert receptor edge index to SparseTensor
    receptor_edge_index = test_geo[i]["receptor"]["edge_index"]
    data["receptor", "receptor"].edge_index = to_sparse_tensor(receptor_edge_index, sparse_sizes=(test_geo[i]["receptor"].num_nodes,)*2)

    Test1.append(data)
# with open("Train.pkl", "wb") as file:
#     pickle.dump(Train, file)
    
# with open("Val.pkl", "wb") as file:
#     pickle.dump(Val, file)
    
# with open("Test.pkl", "wb") as file:
#     pickle.dump(Test, file)
    
# with open("Train1.pkl", "rb") as file:
#     Train= pickle.load(file)
    
# with open("Val.pkl", "rb") as file:
#     Val = pickle.load(file)
    
# with open("Test.pkl", "rb") as file:
#     Test = pickle.load(file)