import os
import sys
import torch
from torch.nn import functional as F
from torch_scatter import scatter_max
import networkx as nx
from rdkit import Chem
from rdkit.Chem import RDConfig, Descriptors
from torchdrug import utils
from torchdrug.layers import functional
from torchdrug.core import Registry as R
from torchdrug.metrics.rdkit import sascorer
[docs]@R.register("metrics.auroc")
def area_under_roc(pred, target):
"""
Area under receiver operating characteristic curve (ROC).
Parameters:
pred (Tensor): predictions of shape :math:`(n,)`
target (Tensor): binary targets of shape :math:`(n,)`
"""
order = pred.argsort(descending=True)
target = target[order]
hit = target.cumsum(0)
all = (target == 0).sum() * (target == 1).sum()
auroc = hit[target == 0].sum() / (all + 1e-10)
return auroc
[docs]@R.register("metrics.auprc")
def area_under_prc(pred, target):
"""
Area under precision-recall curve (PRC).
Parameters:
pred (Tensor): predictions of shape :math:`(n,)`
target (Tensor): binary targets of shape :math:`(n,)`
"""
order = pred.argsort(descending=True)
target = target[order]
precision = target.cumsum(0) / torch.arange(1, len(target) + 1, device=target.device)
auprc = precision[target == 1].sum() / ((target == 1).sum() + 1e-10)
return auprc
[docs]@R.register("metrics.r2")
def r2(pred, target):
"""
:math:`R^2` regression score.
Parameters:
pred (Tensor): predictions of shape :math:`(n,)`
target (Tensor): targets of shape :math:`(n,)`
"""
total = torch.var(target, unbiased=False)
residual = F.mse_loss(pred, target)
return 1 - residual / total
[docs]@R.register("metrics.logp")
def logP(pred):
"""
Logarithm of partition coefficient between octanol and water for a compound.
Parameters:
pred (PackedMolecule): molecules to evaluate
"""
logp = []
for mol in pred:
mol = mol.to_molecule()
try:
with utils.no_rdkit_log():
mol.UpdatePropertyCache()
score = Descriptors.MolLogP(mol)
except Chem.AtomValenceException:
score = 0
logp.append(score)
return torch.tensor(logp, dtype=torch.float, device=pred.device)
[docs]@R.register("metrics.plogp")
def penalized_logP(pred):
"""
Logarithm of partition coefficient, penalized by cycle length and synthetic accessibility.
Parameters:
pred (PackedMolecule): molecules to evaluate
"""
# statistics from ZINC250k
logp_mean = 2.4570953396190123
logp_std = 1.434324401111988
sa_mean = 3.0525811293166134
sa_std = 0.8335207024513095
cycle_mean = 0.0485696876403053
cycle_std = 0.2860212110245455
plogp = []
for mol in pred:
cycles = nx.cycle_basis(nx.Graph(mol.edge_list[:, :2].tolist()))
if cycles:
max_cycle = max([len(cycle) for cycle in cycles])
cycle = max(0, max_cycle - 6)
else:
cycle = 0
mol = mol.to_molecule()
try:
with utils.no_rdkit_log():
mol.UpdatePropertyCache()
Chem.GetSymmSSSR(mol)
logp = Descriptors.MolLogP(mol)
sa = sascorer.calculateScore(mol)
logp = (logp - logp_mean) / logp_std
sa = (sa - sa_mean) / sa_std
cycle = (cycle - cycle_mean) / cycle_std
score = logp - sa - cycle
except Chem.AtomValenceException:
score = -30
plogp.append(score)
return torch.tensor(plogp, dtype=torch.float, device=pred.device)
[docs]@R.register("metrics.SA")
def SA(pred):
"""
Synthetic accesibility score.
Parameters:
pred (PackedMolecule): molecules to evaluate
"""
sa = []
for mol in pred:
with utils.no_rdkit_log():
score = sascorer.calculateScore(mol.to_molecule())
sa.append(score)
return torch.tensor(sa, dtype=torch.float, device=pred.device)
[docs]@R.register("metrics.qed")
def QED(pred):
"""
Quantitative estimation of drug-likeness.
Parameters:
pred (PackedMolecule): molecules to evaluate
"""
qed = []
for mol in pred:
try:
with utils.no_rdkit_log():
score = Descriptors.qed(mol.to_molecule())
except Chem.AtomValenceException:
score = -1
qed.append(score)
return torch.tensor(qed, dtype=torch.float, device=pred.device)
[docs]@R.register("metrics.validity")
def chemical_validity(pred):
"""
Chemical validity of molecules.
Parameters:
pred (PackedMolecule): molecules to evaluate
"""
validity = []
for i, mol in enumerate(pred):
with utils.no_rdkit_log():
smiles = mol.to_smiles()
mol = Chem.MolFromSmiles(smiles)
validity.append(1 if mol else 0)
return torch.tensor(validity, dtype=torch.float, device=pred.device)
[docs]def variadic_accuracy(input, target, size):
"""
Compute classification accuracy over variadic sizes of categories.
Suppose there are :math:`N` samples, and the number of categories in all samples is summed to :math`B`.
Parameters:
input (Tensor): prediction of shape :math:`(B,)`
target (Tensor): target of shape :math:`(N,)`. Each target is a relative index in a sample.
size (Tensor): number of categories of shape :math:`(N,)`
"""
index2graph = functional._size_to_index(size)
input_class = scatter_max(input, index2graph)[1]
target_index = target + size.cumsum(0) - size
accuracy = (input_class == target_index).float()
return accuracy