File size: 3,692 Bytes
e0d0d76 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 |
import numpy as np
import pandas as pd
import pickle
import hydra
import warnings
warnings.filterwarnings('ignore')
import rdkit.Chem as Chem
from rdkit import RDLogger
RDLogger.DisableLog('rdApp.*')
from rdkit.Chem import AllChem, QED
def getReward(name,
receptor_path=None,
pdbqt_path=None,
VinaGPU_path=None,
VinaGPU_config=None):
if name == "QED":
return QEDReward()
elif name == 'DRD2':
with open(hydra.utils.get_original_cwd() + '/Model/QSAR/drd2_qsar_optimized.pkl', mode='rb') as f:
qsar_model = pickle.load(f)
return QSAR_Reward(qsar_model)
elif name == 'AKT1':
with open(hydra.utils.get_original_cwd() + '/Model/QSAR/akt1_qsar_optimized.pkl', mode='rb') as f:
qsar_model = pickle.load(f)
return QSAR_Reward(qsar_model)
class Reward:
def __init__(self):
self.vmin = -100
self.max_r = -10000
return
def reward(self):
raise NotImplementedError()
class QSAR_Reward(Reward):
def __init__(self, qsar_model, *args, **kwargs):
super().__init__(*args, **kwargs)
self.qsar_model = qsar_model
def reward(self, score_que:list = None):
max_smi = None
scores = []
mols = [Chem.MolFromSmiles(smi) for smi in score_que]
ecfps = []
None_indices = []
for i, mol in enumerate(mols):
if mol is not None:
ecfps.append(AllChem.GetMorganFingerprintAsBitVect(mol, 3, nBits=2048))
else:
None_indices.append(i)
ecfps.append([0]*2048)
if len(ecfps) == 0:
return [], None, None
ecfp6_array = np.array(ecfps)
X = pd.DataFrame(ecfp6_array, columns=[f'bit_{i}' for i in range(2048)])
y_pred = self.qsar_model.predict_proba(X)[:, 1]
for None_idx in None_indices:
y_pred[None_idx] = np.nan
max_score = np.nanmax(y_pred)
for smi, score in zip(score_que, y_pred):
if score == np.nan:
pass
elif score == max_score:
max_smi = smi
scores.append((smi, score))
return scores, max_smi, max_score
def reward_remove_nan(self, score_que:list = None):
max_smi = None
scores = []
# convert smiles to mol if mol is not none.
valid_smiles = []
mols = []
for smi in score_que:
mol = Chem.MolFromSmiles(smi)
if mol is not None:
valid_smiles.append(smi)
mols.append(mol)
ecfps = []
for i, mol in enumerate(mols):
if mol is not None:
ecfps.append(AllChem.GetMorganFingerprintAsBitVect(mol, 3, nBits=2048))
if len(ecfps) == 0:
return [], None, None
ecfp6_array = np.array(ecfps)
X = pd.DataFrame(ecfp6_array, columns=[f'bit_{i}' for i in range(2048)])
y_pred = self.qsar_model.predict_proba(X)[:, 1]
max_score = np.nanmax(y_pred)
for smi, score in zip(valid_smiles, y_pred):
if score == max_score:
max_smi = smi
scores.append((smi, score))
return scores, max_smi, max_score
class QEDReward(Reward):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.vmin = 0
def reward(self, smi):
mol = Chem.MolFromSmiles(smi)
try:
score = QED.qed(mol)
except:
score = None
return score |