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import torch
import pandas as pd
from rdkit import Chem, rdBase
from torch_geometric.data import Data
from torch.utils.data import Dataset, random_split
rdBase.DisableLog("rdApp.*")
def one_of_k_encoding(x, allowable_set):
# last position - unknown
if x not in allowable_set:
x = allowable_set[-1]
return list(map(lambda s: x == s, allowable_set))
def get_atom_features(atom):
symbols_list = [
"C",
"N",
"O",
"S",
"F",
"Si",
"P",
"Cl",
"Br",
"Mg",
"Na",
"Ca",
"Fe",
"As",
"Al",
"I",
"B",
"V",
"K",
"Tl",
"Yb",
"Sb",
"Sn",
"Ag",
"Pd",
"Co",
"Se",
"Ti",
"Zn",
"H",
"Li",
"Ge",
"Cu",
"Au",
"Ni",
"Cd",
"In",
"Mn",
"Zr",
"Cr",
"Pt",
"Hg",
"Pb",
"Unknown",
]
degrees_list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
numhs_list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
implicit_valences_list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
formal_charge_list = [-2, -1, 0, 1, 2]
chirality_list = [
Chem.rdchem.ChiralType.CHI_UNSPECIFIED,
Chem.rdchem.ChiralType.CHI_TETRAHEDRAL_CW,
Chem.rdchem.ChiralType.CHI_TETRAHEDRAL_CCW,
Chem.rdchem.ChiralType.CHI_OTHER,
]
return np.array(
# Type of atom (Symbol)
one_of_k_encoding(atom.GetSymbol(), symbols_list)
+
# Number of neighbours (Degree)
one_of_k_encoding(atom.GetDegree(), degrees_list)
+
# Number of hydrogen atoms (Implicit Hs) - bond donors
one_of_k_encoding(atom.GetTotalNumHs(), numhs_list)
+
# Valence - chemical potential
one_of_k_encoding(atom.GetImplicitValence(), implicit_valences_list)
+
# Hybridization - so important for 3d structure, sp2 - Trigonal planar, sp3 - Tetrahedral
one_of_k_encoding(
atom.GetHybridization(),
[
Chem.rdchem.HybridizationType.SP,
Chem.rdchem.HybridizationType.SP2,
Chem.rdchem.HybridizationType.SP3,
Chem.rdchem.HybridizationType.SP3D,
Chem.rdchem.HybridizationType.SP3D2,
"other",
],
)
+
# Aromaticity (Boolean)
[atom.GetIsAromatic()]
+
# Formal Charge
one_of_k_encoding(atom.GetFormalCharge(), formal_charge_list)
+
# Chirality (Geometry)
one_of_k_encoding(atom.GetChiralTag(), chirality_list)
+
# Is in ring (Boolean)
[atom.IsInRing()]
)
def get_protein_features(char):
prot_vocab = {
"A": 1,
"R": 2,
"N": 3,
"D": 4,
"C": 5,
"Q": 6,
"E": 7,
"G": 8,
"H": 9,
"I": 10,
"L": 11,
"K": 12,
"M": 13,
"F": 14,
"P": 15,
"S": 16,
"T": 17,
"W": 18,
"Y": 19,
"V": 20,
"X": 21,
"Z": 21,
"B": 21,
"PAD": 0,
"UNK": 21,
}
return prot_vocab.get(char, prot_vocab["UNK"])
class BindingDataset(Dataset):
def __init__(self, dataframe, max_seq_length=1000):
self.data = dataframe
self.max_seq_length = (
max_seq_length # Define a maximum sequence length for padding/truncation
)
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
row = self.data.iloc[idx]
smiles = row["smiles"]
sequence = row["sequence"]
affinity = row["affinity"]
mol = Chem.MolFromSmiles(smiles)
if mol is None:
return None
# Ligand (Graph)
# Nodes
atom_features = [get_atom_features(atom) for atom in mol.GetAtoms()]
x = torch.tensor(np.array(atom_features), dtype=torch.float)
# Edges
edge_indexes = []
for bond in mol.GetBonds():
i = bond.GetBeginAtomIdx()
j = bond.GetEndAtomIdx()
edge_indexes.append((i, j))
edge_indexes.append((j, i))
# t - transpose, [num_of_edges, 2] -> [2, num_of_edges]
# contiguous - take the virtually transposed tensor and make its physical copy and lay bytes sequentially
edge_index = torch.tensor(edge_indexes, dtype=torch.long).t().contiguous()
# Protein (Sequence, tensor of integers)
tokens = [get_protein_features(char) for char in sequence]
if len(tokens) > self.max_seq_length:
tokens = tokens[: self.max_seq_length]
else:
tokens.extend(
[get_protein_features("PAD")] * (self.max_seq_length - len(tokens))
)
protein_tensor = torch.tensor(tokens, dtype=torch.long)
# Affinity
y = torch.tensor([affinity], dtype=torch.float)
return Data(x=x, edge_index=edge_index, protein_seq=protein_tensor, y=y)
if __name__ == "__main__":
dataset = pd.read_csv("pdbbind_refined_dataset.csv")
dataset = BindingDataset(dataset)
train_size = int(0.8 * len(dataset))
test_size = len(dataset) - train_size
train_dataset, test_dataset = random_split(dataset, [train_size, test_size])
print(len(train_dataset))
print(len(test_dataset))
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