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May 6, 2021 18:12
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basic wandb integration for deepchem protein-ligand tutorial
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#! /usr/bin/env python | |
import deepchem as dc | |
from deepchem.utils import download_url | |
from deepchem.utils.evaluate import Evaluator | |
import pandas as pd | |
import nglview | |
import tempfile | |
import os | |
import mdtraj as md | |
import numpy as np | |
import wandb | |
import os | |
from sklearn.ensemble import RandomForestRegressor | |
def convert_lines_to_mdtraj(molecule_lines, fname): | |
molecule_lines = molecule_lines.strip('[').strip(']').replace("'","").replace("\\n", "").split(", ") | |
# tempdir = tempfile.mkdtemp() | |
# molecule_file = os.path.join(tempdir, "molecule.pdb") | |
molecule_file = fname | |
with open(molecule_file, "w") as f: | |
for line in molecule_lines: | |
f.write("%s\n" % line) | |
# molecule_mdtraj = md.load(molecule_file) | |
# return molecule_mdtraj | |
return molecule_file | |
def combine_mdtraj(protein, ligand): | |
chain = protein.topology.add_chain() | |
residue = protein.topology.add_residue("LIG", chain, resSeq=1) | |
for atom in ligand.topology.atoms: | |
protein.topology.add_atom(atom.name, atom.element, residue) | |
protein.xyz = np.hstack([protein.xyz, ligand.xyz]) | |
protein.topology.create_standard_bonds() | |
return protein | |
data_dir = dc.utils.get_data_dir() | |
dataset_file = os.path.join(data_dir, "pdbbind_core_df.csv.gz") | |
if not os.path.exists(dataset_file): | |
print('File does not exist. Downloading file...') | |
download_url("https://s3-us-west-1.amazonaws.com/deepchem.io/datasets/pdbbind_core_df.csv.gz") | |
print('File downloaded...') | |
raw_dataset = dc.utils.save.load_from_disk(dataset_file) | |
def make_pdb(prefix, _id): | |
return prefix + "_" + str(_id) + ".pdb" | |
def log_protein(_id): | |
first_protein, first_ligand = raw_dataset.iloc[_id]["protein_pdb"], raw_dataset.iloc[_id]["ligand_pdb"] | |
protein_mdtraj = convert_lines_to_mdtraj(first_protein, make_pdb("prot", _id)) | |
ligand_mdtraj = convert_lines_to_mdtraj(first_ligand, make_pdb("lig", _id)) | |
complex_mdtraj = combine_mdtraj(md.load(protein_mdtraj), md.load(ligand_mdtraj)) | |
complex_pdb = complex_mdtraj.save(make_pdb("complex", _id)) | |
wandb.log({"protein" : wandb.Molecule(protein_mdtraj), | |
"ligand" : wandb.Molecule(ligand_mdtraj), | |
"combo" : wandb.Molecule(make_pdb("complex", _id))}) | |
def log(): | |
for _id in range(21, 25): | |
wandb.init(project="deepchem_interact", entity="stacey", reinit=True, name="cling rc test "+str(_id)) | |
log_protein(_id) | |
print("LOGGED PROTEIN: ", str(_id)) | |
#log() | |
def train(): | |
#wandb.init(project="deepchem_interact", entity="stacey") | |
grid_featurizer = dc.feat.RdkitGridFeaturizer( | |
voxel_width=16.0, feature_types=["ecfp", "splif", "hbond", "pi_stack", "cation_pi", "salt_bridge"], | |
ecfp_power=5, splif_power=5, parallel=True, flatten=True, sanitize=True) | |
compound_featurizer = dc.feat.CircularFingerprint(size=128) | |
pdbbind_tasks, (train_dataset, valid_dataset, test_dataset), transformers = dc.molnet.load_pdbbind_grid( | |
featurizer="ECFP", subset="refined") | |
seed=23 # Set a random seed to get stable results | |
cfg = {"max_features" : 100, "model_type" : "multitask_reg"} | |
wandb.init(project="deepchem_interact", name="cling rc test", config=cfg, reinit=True) | |
# try a deep model | |
#dnn = dc.models.MultitaskRegressor(1, [[100) | |
# train model | |
dnn.fit(train_dataset, nb_epoch=10) | |
# Evaluate trained model on validation data and log R^2 to W&B | |
metric = dc.metrics.Metric(dc.metrics.r2_score) | |
dnn_test_evaluator = Evaluator(dnn, valid_dataset, transformers) | |
dnn_test_r2score = dnn_test_evaluator.compute_model_performance([metric]) | |
print("DNN Test set R^2 %f" % (dnn_test_r2score["r2_score"])) | |
wandb.log({"r2" : dnn_test_r2score["r2_score"]}) | |
def rf(): | |
#rf = [10, 50, 75, 100] | |
#mf = ["auto", "sqrt", "log2", None] | |
grid_featurizer = dc.feat.RdkitGridFeaturizer( | |
voxel_width=16.0, feature_types=["ecfp", "splif", "hbond", "pi_stack", "cation_pi", "salt_bridge"], | |
ecfp_power=5, splif_power=5, parallel=True, flatten=True, sanitize=True) | |
compound_featurizer = dc.feat.CircularFingerprint(size=128) | |
pdbbind_tasks, (train_dataset, valid_dataset, test_dataset), transformers = dc.molnet.load_pdbbind_grid( | |
featurizer="ECFP", subset="refined") | |
rf = [150, 300, 10, 20] | |
mf = ["auto", "sqrt"] | |
for n in rf: | |
for m in mf: | |
print("M: ", m, "N: ", n) | |
cfg = {"n_estimators" : n, "max_features" : m, "model_type" : "random forest"} | |
wandb.init(project="deepchem_interact", name="cling rc rf test", config=cfg, reinit=True) | |
# + str(n) + " Est - Max Feat " + str(m), config=cfg, reinit=True) | |
seed=23 # Set a random seed to get stable results | |
sklearn_model = RandomForestRegressor(n_estimators=n, max_features=m) | |
sklearn_model.random_state = seed | |
model = dc.models.SklearnModel(sklearn_model) | |
model.fit(train_dataset) | |
wandb.sklearn.plot_learning_curve(model.model_instance, train_dataset.X, train_dataset.y.ravel()) | |
#sklearn_curve( | |
metric = dc.metrics.Metric(dc.metrics.r2_score) | |
evaluator = Evaluator(model, train_dataset, transformers) | |
train_r2score = evaluator.compute_model_performance([metric]) | |
print("RF Train set R^2 %f" % (train_r2score["r2_score"])) | |
evaluator = Evaluator(model, valid_dataset, transformers) | |
valid_r2score = evaluator.compute_model_performance([metric]) | |
print("RF Valid set R^2 %f" % (valid_r2score["r2_score"])) | |
wandb.log({"train r2" : train_r2score, "val r2" : valid_r2score}) |
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