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Predicting

Predicting#

Import packages#

[1]:

import pandas as pd
import numpy as np
import torch
from lightning import pytorch as pl
from pathlib import Path

from chemprop import data, featurizers, models

Change model input here#

[2]:

chemprop_dir = Path.cwd().parent
checkpoint_path = chemprop_dir / "tests" / "data" / "example_model_v2_regression_mol.ckpt" # path to the checkpoint file.
# If the checkpoint file is generated using the training notebook, it will be in the `checkpoints` folder with name similar to `checkpoints/epoch=19-step=180.ckpt`.

Load model#

[3]:

mpnn = models.MPNN.load_from_checkpoint(checkpoint_path)
mpnn

/home/hwpang/miniforge3/envs/chemprop_v2_dev/lib/python3.11/site-packages/lightning/pytorch/utilities/parsing.py:199: Attribute 'graph_transform' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['graph_transform'])`.
/home/hwpang/miniforge3/envs/chemprop_v2_dev/lib/python3.11/site-packages/lightning/pytorch/utilities/parsing.py:199: Attribute 'output_transform' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['output_transform'])`.

[3]:

MPNN(
  (message_passing): BondMessagePassing(
    (W_i): Linear(in_features=86, out_features=300, bias=False)
    (W_h): Linear(in_features=300, out_features=300, bias=False)
    (W_o): Linear(in_features=372, out_features=300, bias=True)
    (W_d): Linear(in_features=300, out_features=300, bias=True)
    (dropout): Dropout(p=0.0, inplace=False)
    (tau): ReLU()
    (V_d_transform): Identity()
    (graph_transform): GraphTransform(
      (V_transform): Identity()
      (E_transform): Identity()
    )
  )
  (agg): MeanAggregation()
  (bn): BatchNorm1d(300, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (predictor): RegressionFFN(
    (ffn): MLP(
      (0): Sequential(
        (0): Linear(in_features=300, out_features=300, bias=True)
      )
      (1): Sequential(
        (0): ReLU()
        (1): Dropout(p=0.0, inplace=False)
        (2): Linear(in_features=300, out_features=1, bias=True)
      )
    )
    (criterion): MSELoss()
    (output_transform): UnscaleTransform()
  )
  (X_d_transform): Identity()
)

Change predict input here#

[4]:

chemprop_dir = Path.cwd().parent
test_path = chemprop_dir / "tests" / "data" / "regression" / "mol" / "mol.csv"
smiles_column = 'smiles'

Load test smiles#

[5]:

df_test = pd.read_csv(test_path)
df_test

[5]:
smiles lipo
0 Cn1c(CN2CCN(CC2)c3ccc(Cl)cc3)nc4ccccc14 3.54
1 COc1cc(OC)c(cc1NC(=O)CSCC(=O)O)S(=O)(=O)N2C(C)... -1.18
2 COC(=O)[C@@H](N1CCc2sccc2C1)c3ccccc3Cl 3.69
3 OC[C@H](O)CN1C(=O)C(Cc2ccccc12)NC(=O)c3cc4cc(C... 3.37
4 Cc1cccc(C[C@H](NC(=O)c2cc(nn2C)C(C)(C)C)C(=O)N... 3.10
... ... ...
95 CC(C)N(CCCNC(=O)Nc1ccc(cc1)C(C)(C)C)C[C@H]2O[C... 2.20
96 CCN(CC)CCCCNc1ncc2CN(C(=O)N(Cc3cccc(NC(=O)C=C)... 2.04
97 CCSc1c(Cc2ccccc2C(F)(F)F)sc3N(CC(C)C)C(=O)N(C)... 4.49
98 COc1ccc(Cc2c(N)n[nH]c2N)cc1 0.20
99 CCN(CCN(C)C)S(=O)(=O)c1ccc(cc1)c2cnc(N)c(n2)C(... 2.00

100 rows × 2 columns

Get smiles#

[6]:

smis = df_test[smiles_column]
smis

[6]:

0               Cn1c(CN2CCN(CC2)c3ccc(Cl)cc3)nc4ccccc14
1     COc1cc(OC)c(cc1NC(=O)CSCC(=O)O)S(=O)(=O)N2C(C)...
2                COC(=O)[C@@H](N1CCc2sccc2C1)c3ccccc3Cl
3     OC[C@H](O)CN1C(=O)C(Cc2ccccc12)NC(=O)c3cc4cc(C...
4     Cc1cccc(C[C@H](NC(=O)c2cc(nn2C)C(C)(C)C)C(=O)N...
                            ...
95    CC(C)N(CCCNC(=O)Nc1ccc(cc1)C(C)(C)C)C[C@H]2O[C...
96    CCN(CC)CCCCNc1ncc2CN(C(=O)N(Cc3cccc(NC(=O)C=C)...
97    CCSc1c(Cc2ccccc2C(F)(F)F)sc3N(CC(C)C)C(=O)N(C)...
98                          COc1ccc(Cc2c(N)n[nH]c2N)cc1
99    CCN(CCN(C)C)S(=O)(=O)c1ccc(cc1)c2cnc(N)c(n2)C(...
Name: smiles, Length: 100, dtype: object

Get molecule datapoints#

[7]:

test_data = [data.MoleculeDatapoint.from_smi(smi) for smi in smis]

Get molecule dataset#

[8]:

featurizer = featurizers.SimpleMoleculeMolGraphFeaturizer()
test_dset = data.MoleculeDataset(test_data, featurizer=featurizer)
test_loader = data.build_dataloader(test_dset, shuffle=False)

Set up trainer#

[9]:

with torch.inference_mode():
    trainer = pl.Trainer(
        logger=None,
        enable_progress_bar=True,
        accelerator="cpu",
        devices=1
    )
    test_preds = trainer.predict(mpnn, test_loader)

/home/hwpang/miniforge3/envs/chemprop_v2_dev/lib/python3.11/site-packages/lightning/fabric/plugins/environments/slurm.py:204: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python /home/hwpang/miniforge3/envs/chemprop_v2_dev/lib/pyt ...
GPU available: True (cuda), used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
/home/hwpang/miniforge3/envs/chemprop_v2_dev/lib/python3.11/site-packages/lightning/pytorch/trainer/setup.py:187: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.
/home/hwpang/miniforge3/envs/chemprop_v2_dev/lib/python3.11/site-packages/lightning/fabric/plugins/environments/slurm.py:204: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python /home/hwpang/miniforge3/envs/chemprop_v2_dev/lib/pyt ...
/home/hwpang/miniforge3/envs/chemprop_v2_dev/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=63` in the `DataLoader` to improve performance.

Predicting DataLoader 0: 100%|██████████| 100/100 [00:00<00:00, 396.76it/s]

[10]:

test_preds = np.concatenate(test_preds, axis=0)
df_test['pred'] = test_preds
df_test

[10]:
smiles lipo pred
0 Cn1c(CN2CCN(CC2)c3ccc(Cl)cc3)nc4ccccc14 3.54 2.176904
1 COc1cc(OC)c(cc1NC(=O)CSCC(=O)O)S(=O)(=O)N2C(C)... -1.18 2.148450
2 COC(=O)[C@@H](N1CCc2sccc2C1)c3ccccc3Cl 3.69 2.159459
3 OC[C@H](O)CN1C(=O)C(Cc2ccccc12)NC(=O)c3cc4cc(C... 3.37 2.167359
4 Cc1cccc(C[C@H](NC(=O)c2cc(nn2C)C(C)(C)C)C(=O)N... 3.10 2.153605
... ... ... ...
95 CC(C)N(CCCNC(=O)Nc1ccc(cc1)C(C)(C)C)C[C@H]2O[C... 2.20 2.149804
96 CCN(CC)CCCCNc1ncc2CN(C(=O)N(Cc3cccc(NC(=O)C=C)... 2.04 2.153695
97 CCSc1c(Cc2ccccc2C(F)(F)F)sc3N(CC(C)C)C(=O)N(C)... 4.49 2.158461
98 COc1ccc(Cc2c(N)n[nH]c2N)cc1 0.20 2.175282
99 CCN(CCN(C)C)S(=O)(=O)c1ccc(cc1)c2cnc(N)c(n2)C(... 2.00 2.159477

100 rows × 3 columns

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