From 565fb2f91a13e292a45d902f8da09e455e128c5b Mon Sep 17 00:00:00 2001 From: Bharath Ramsundar Date: Mon, 1 Jun 2020 12:44:59 -0700 Subject: [PATCH] Changes --- examples/data_loading/README.md | 4 + examples/data_loading/example.csv | 11 + .../data_loading/membrane_permeability.sdf | 1165 +++++++++++++++++ examples/data_loading/pandas_csv.py | 22 + examples/datasets/README.md | 3 + examples/datasets/pretty_print.py | 5 + examples/datasets/scaffold_split_print.py | 17 + examples/delaney/README.md | 15 + examples/delaney/__init__.py | 0 examples/delaney/delaney_chemception.py | 35 + examples/factors/README.md | 23 + examples/hiv/README.md | 23 + examples/hiv/__init__.py | 0 examples/hopv/README.md | 15 + examples/kaggle/README.md | 21 + examples/kaggle/__init__.py | 0 examples/kinase/README.md | 24 + examples/model_restore/README.md | 26 + examples/model_restore/chemception_model.py | 35 + examples/model_restore/chemception_restore.py | 16 + examples/muv/__init__.py | 0 examples/nci/__init__.py | 0 examples/pcba/__init__.py | 0 examples/pdbbind/__init__.py | 0 examples/pretraining/README.md | 13 + examples/pretraining/fcnet_pretraining.py | 52 + examples/qm7/README.md | 55 + examples/qm7/__init__.py | 0 examples/qm9/__init__.py | 0 examples/sampl/__init__.py | 0 examples/sider/__init__.py | 0 examples/splitters/README.md | 27 + examples/splitters/random_split.py | 17 + examples/splitters/scaffold_split.py | 17 + examples/sweetlead/README.md | 10 + examples/tests.py | 39 - examples/tox21/__init__.py | 0 examples/toxcast/README.md | 19 + examples/toxcast/__init__.py | 0 examples/transformers/README.md | 17 + examples/uv/README.md | 19 + 41 files changed, 1706 insertions(+), 39 deletions(-) create mode 100644 examples/data_loading/README.md create mode 100644 examples/data_loading/example.csv create mode 100644 examples/data_loading/membrane_permeability.sdf create mode 100644 examples/data_loading/pandas_csv.py create mode 100644 examples/datasets/README.md create mode 100644 examples/datasets/pretty_print.py create mode 100644 examples/datasets/scaffold_split_print.py create mode 100644 examples/delaney/README.md delete mode 100644 examples/delaney/__init__.py create mode 100644 examples/delaney/delaney_chemception.py create mode 100644 examples/factors/README.md create mode 100644 examples/hiv/README.md delete mode 100644 examples/hiv/__init__.py create mode 100644 examples/hopv/README.md create mode 100644 examples/kaggle/README.md delete mode 100644 examples/kaggle/__init__.py create mode 100644 examples/kinase/README.md create mode 100644 examples/model_restore/README.md create mode 100644 examples/model_restore/chemception_model.py create mode 100644 examples/model_restore/chemception_restore.py delete mode 100644 examples/muv/__init__.py delete mode 100644 examples/nci/__init__.py delete mode 100644 examples/pcba/__init__.py delete mode 100644 examples/pdbbind/__init__.py create mode 100644 examples/pretraining/README.md create mode 100644 examples/pretraining/fcnet_pretraining.py create mode 100644 examples/qm7/README.md delete mode 100644 examples/qm7/__init__.py delete mode 100644 examples/qm9/__init__.py delete mode 100644 examples/sampl/__init__.py delete mode 100644 examples/sider/__init__.py create mode 100644 examples/splitters/README.md create mode 100644 examples/splitters/random_split.py create mode 100644 examples/splitters/scaffold_split.py create mode 100644 examples/sweetlead/README.md delete mode 100644 examples/tests.py delete mode 100644 examples/tox21/__init__.py create mode 100644 examples/toxcast/README.md delete mode 100644 examples/toxcast/__init__.py create mode 100644 examples/transformers/README.md create mode 100644 examples/uv/README.md diff --git a/examples/data_loading/README.md b/examples/data_loading/README.md new file mode 100644 index 0000000000..f759ef99bb --- /dev/null +++ b/examples/data_loading/README.md @@ -0,0 +1,4 @@ +# Data Loading Examples + +The examples in this directory highlight a number of ways to +load datasets into DeepChem for downstream analysis. diff --git a/examples/data_loading/example.csv b/examples/data_loading/example.csv new file mode 100644 index 0000000000..50eb50f8bf --- /dev/null +++ b/examples/data_loading/example.csv @@ -0,0 +1,11 @@ +Compound ID,log-solubility,smiles +Amigdalin,0.9740000000000001,OCC3OC(OCC2OC(OC(C#N)c1ccccc1)C(O)C(O)C2O)C(O)C(O)C3O +Fenfuram,2.885,Cc1occc1C(=O)Nc2ccccc2 +citral,2.5789999999999997,CC(C)=CCCC(C)=CC(=O) +Picene,6.617999999999999,c1ccc2c(c1)ccc3c2ccc4c5ccccc5ccc43 +Thiophene,2.2319999999999998,c1ccsc1 +benzothiazole,2.733,c2ccc1scnc1c2 +"2,2,4,6,6'-PCB",6.545,Clc1cc(Cl)c(c(Cl)c1)c2c(Cl)cccc2Cl +Estradiol,4.138,CC12CCC3C(CCc4cc(O)ccc34)C2CCC1O +Dieldrin,4.533,ClC4=C(Cl)C5(Cl)C3C1CC(C2OC12)C3C4(Cl)C5(Cl)Cl +Rotenone,5.246,COc5cc4OCC3Oc2c1CC(Oc1ccc2C(=O)C3c4cc5OC)C(C)=C diff --git a/examples/data_loading/membrane_permeability.sdf b/examples/data_loading/membrane_permeability.sdf new file mode 100644 index 0000000000..374bb08c2c --- /dev/null +++ b/examples/data_loading/membrane_permeability.sdf @@ -0,0 +1,1165 @@ +10_filipski_40 + RDKit 3D + + 48 50 0 0 1 0 0 0 0 0999 V2000 + 9.1378 -7.4697 -1.1731 C 0 0 0 0 0 0 0 0 0 0 0 0 + 9.0300 -8.7563 -1.7553 C 0 0 0 0 0 0 0 0 0 0 0 0 + 10.1829 -9.4791 -2.1168 C 0 0 0 0 0 0 0 0 0 0 0 0 + 11.4593 -8.9144 -1.9184 C 0 0 0 0 0 0 0 0 0 0 0 0 + 11.5888 -7.6306 -1.3431 C 0 0 0 0 0 0 0 0 0 0 0 0 + 10.4211 -6.9229 -0.9733 C 0 0 0 0 0 0 0 0 0 0 0 0 + 8.0685 -6.6893 -0.7812 O 0 0 0 0 0 0 0 0 0 0 0 0 + 6.7356 -7.1730 -0.9323 C 0 0 0 0 0 0 0 0 0 0 0 0 + 5.8194 -5.9457 -0.8867 C 0 0 0 0 0 0 0 0 0 0 0 0 + 6.3937 -8.1606 0.1955 C 0 0 0 0 0 0 0 0 0 0 0 0 + 10.0417 -10.7213 -2.6806 O 0 0 0 0 0 0 0 0 0 0 0 0 + 10.6226 -11.7880 -2.0428 C 0 0 0 0 0 0 0 0 0 0 0 0 + 11.4794 -12.6365 -2.7738 C 0 0 0 0 0 0 0 0 0 0 0 0 + 12.0777 -13.7503 -2.1503 C 0 0 0 0 0 0 0 0 0 0 0 0 + 11.8056 -14.0231 -0.7953 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object. This may be useful if you +# want the flexibility of processing your data with Pandas +# directly. +import pandas as pd +import deepchem as dc + +df = pd.read_csv("example.csv") +print("Original data loaded as DataFrame:") +print(df) + +featurizer = dc.feat.CircularFingerprint(size=16) +features = featurizer.featurize(df["smiles"]) +dataset = dc.data.NumpyDataset(X=features, y=df["log-solubility"], ids=df["Compound ID"]) + +print("Data converted into DeepChem Dataset") +print(dataset) + +# Now let's convert from a dataset back to a pandas dataframe +converted_df = dataset.to_dataframe() +print("Data converted back into DataFrame:") +print(converted_df) diff --git a/examples/datasets/README.md b/examples/datasets/README.md new file mode 100644 index 0000000000..b40d573c01 --- /dev/null +++ b/examples/datasets/README.md @@ -0,0 +1,3 @@ +# Dataset Examples + +This folder countains examples of using DeepChem datasets to do things. diff --git a/examples/datasets/pretty_print.py b/examples/datasets/pretty_print.py new file mode 100644 index 0000000000..4db8ae32fa --- /dev/null +++ b/examples/datasets/pretty_print.py @@ -0,0 +1,5 @@ +import numpy as np +import deepchem as dc + +dataset = dc.data.NumpyDataset(np.random.rand(500, 5)) +print(dataset) diff --git a/examples/datasets/scaffold_split_print.py b/examples/datasets/scaffold_split_print.py new file mode 100644 index 0000000000..f3987b332b --- /dev/null +++ b/examples/datasets/scaffold_split_print.py @@ -0,0 +1,17 @@ +import deepchem as dc + +mols = ['C1=CC2=C(C=C1)C1=CC=CC=C21', 'O=C1C=CC(=O)C2=C1OC=CO2', 'C1=C[N]C=C1', 'C1=CC=CC=C[C+]1', 'C1=[C]NC=C1', 'N[C@@H](C)C(=O)O', 'N[C@H](C)C(=O)O', 'CC', 'O=C=O', 'C#N', 'CCN(CC)CC', 'CC(=O)O', 'C1CCCCC1', 'c1ccccc1'] +print("Original set of molecules") +print(mols) + +splitter = dc.splits.ScaffoldSplitter(seed=123) +train, valid, test = splitter.train_valid_test_split(mols) +# The return values are dc.data.Dataset objects so we need to extract +# the ids +print("Training set") +print(train) +print("Valid set") +print(valid) +print("Test set") +print(test) + diff --git a/examples/delaney/README.md b/examples/delaney/README.md new file mode 100644 index 0000000000..6699a0c76d --- /dev/null +++ b/examples/delaney/README.md @@ -0,0 +1,15 @@ +The Delaney dataset is a collection of 2874 aqueous solubility measurements from this paper: + +Delaney, John S. "ESOL: estimating aqueous solubility directly from molecular structure." Journal of chemical information and computer sciences 44.3 (2004): 1000-1005. + +This dataset is commonly used since it's a small molecular +regression dataset that's convenient for benchmarking various +techniques. In this example, we train a series of different +DeepChem models against this task: + +- `DAGModel`: In `delaney_DAG.py`. This model will train and +converge very slowly. +- `TextCNNModel`: In `delaney_textcnn.py`. This model featurizes compounds as SMILES strings directly and trains a convolutional network directly on the text. +- `WeaveModel`: In `delaney_weave.py`. This model trains a weave style convolution on Delaney. +- `ChemCeption`: In `delaney_chemception.py`. This model trains a variant of an Inception convolutional network on images generated from molecules. +- `MPNNModel`: In `delaney_MPNN.py`. This model trains a little slower, but is faster than `DAGModel`. diff --git a/examples/delaney/__init__.py b/examples/delaney/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/examples/delaney/delaney_chemception.py b/examples/delaney/delaney_chemception.py new file mode 100644 index 0000000000..57ea1cf0b6 --- /dev/null +++ b/examples/delaney/delaney_chemception.py @@ -0,0 +1,35 @@ +""" +Script that trains Chemception models on delaney dataset. +""" +import numpy as np +np.random.seed(123) +import tensorflow as tf +tf.random.set_seed(123) +import deepchem as dc + +# Load Delaney dataset +delaney_tasks, delaney_datasets, transformers = dc.molnet.load_delaney( + featurizer='smiles2img', split='index', img_spec="engd") +train_dataset, valid_dataset, test_dataset = delaney_datasets + +# Get Metric +metric = dc.metrics.Metric(dc.metrics.pearson_r2_score, np.mean) + +model = dc.models.ChemCeption( + img_spec="engd", + n_tasks=len(delaney_tasks), + model_dir=None, + mode="regression") + +# Fit trained model +model.fit(train_dataset, nb_epoch=50) + +print("Evaluating model") +train_scores = model.evaluate(train_dataset, [metric], transformers) +valid_scores = model.evaluate(valid_dataset, [metric], transformers) + +print("Train scores") +print(train_scores) + +print("Validation scores") +print(valid_scores) diff --git a/examples/factors/README.md b/examples/factors/README.md new file mode 100644 index 0000000000..f91adde019 --- /dev/null +++ b/examples/factors/README.md @@ -0,0 +1,23 @@ +# Factors Examples + +The Factors dataset is an in-house dataset from Merck that was first introduced in the following paper: + +Ramsundar, Bharath, et al. "Is multitask deep learning practical for pharma?." Journal of chemical information and modeling 57.8 (2017): 2068-2076. + +It contains 1500 Merck in-house compounds that were measured +for IC50 of inhibition on 12 serine proteases. Unlike most of +the other datasets featured in MoleculeNet, the Factors +collection does not have structures for the compounds tested +since they were proprietary Merck compounds. However, the +collection does feature pre-computed descriptors for these +compounds. + +Note that the original train/valid/test split from the source +data was preserved here, so this function doesn't allow for +alternate modes of splitting. Similarly, since the source data +came pre-featurized, it is not possible to apply alternative +featurizations. + +In this example, we train various models on the Factors dataset: + +- diff --git a/examples/hiv/README.md b/examples/hiv/README.md new file mode 100644 index 0000000000..8e4b53834d --- /dev/null +++ b/examples/hiv/README.md @@ -0,0 +1,23 @@ +# HIV Dataset Examples + +The HIV dataset was introduced by the Drug Therapeutics +Program (DTP) AIDS Antiviral Screen, which tested the ability +to inhibit HIV replication for over 40,000 compounds. +Screening results were evaluated and placed into three +categories: confirmed inactive (CI),confirmed active (CA) and +confirmed moderately active (CM). We further combine the +latter two labels, making it a classification task between +inactive (CI) and active (CA and CM). + +The data file contains a csv table, in which columns below +are used: +- "smiles": SMILES representation of the molecular structure +- "activity": Three-class labels for screening results: CI/CM/CA +- "HIV_active": Binary labels for screening results: 1 (CA/CM) and 0 (CI) + +References: +AIDS Antiviral Screen Data. https://wiki.nci.nih.gov/display/NCIDTPdata/AIDS+Antiviral+Screen+Data + +## Models Trained + +In this example we train the following models on the HIV collection. diff --git a/examples/hiv/__init__.py b/examples/hiv/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/examples/hopv/README.md b/examples/hopv/README.md new file mode 100644 index 0000000000..ef7a12a80a --- /dev/null +++ b/examples/hopv/README.md @@ -0,0 +1,15 @@ +# Harvard Organic Photovoltaic Dataset + +The HOPV datasets consist of the "Harvard Organic +Photovoltaic Dataset. This dataset includes 350 small +molecules and polymers that were utilized as p-type materials +in OPVs. Experimental properties include: HOMO [a.u.], LUMO +[a.u.], Electrochemical gap [a.u.], Optical gap [a.u.], Power +conversion efficiency [%], Open circuit potential [V], Short +circuit current density [mA/cm^2], and fill factor [%]. +Theoretical calculations in the original dataset have been +removed (for now). + +Lopez, Steven A., et al. "The Harvard organic photovoltaic dataset." Scientific data 3.1 (2016): 1-7. + +In this example, we train models on the HOPV dataset to predict these properties. diff --git a/examples/kaggle/README.md b/examples/kaggle/README.md new file mode 100644 index 0000000000..bc797d6ab1 --- /dev/null +++ b/examples/kaggle/README.md @@ -0,0 +1,21 @@ +# Kaggle Dataset Examples + +The Kaggle dataset is an in-house dataset from Merck that was first introduced in the following paper: + +Ma, Junshui, et al. "Deep neural nets as a method for quantitative structure–activity relationships." Journal of chemical information and modeling 55.2 (2015): 263-274. + +It contains 100,000 unique Merck in-house compounds that were +measured on 15 enzyme inhibition and ADME/TOX datasets. +Unlike most of the other datasets featured in MoleculeNet, +the Kaggle collection does not have structures for the +compounds tested since they were proprietary Merck compounds. +However, the collection does feature pre-computed descriptors +for these compounds. + +Note that the original train/valid/test split from the source +data was preserved here, so this function doesn't allow for +alternate modes of splitting. Similarly, since the source data +came pre-featurized, it is not possible to apply alternative +featurizations. + +This folder contains examples training models on the Kaggle dataset: diff --git a/examples/kaggle/__init__.py b/examples/kaggle/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/examples/kinase/README.md b/examples/kinase/README.md new file mode 100644 index 0000000000..f54c5b17fa --- /dev/null +++ b/examples/kinase/README.md @@ -0,0 +1,24 @@ +# README for Kinase Example + +The Kinase dataset is an in-house dataset from Merck that was first introduced in the following paper: + +Ramsundar, Bharath, et al. "Is multitask deep learning practical for pharma?." Journal of chemical information and modeling 57.8 (2017): 2068-2076. + +It contains 2500 Merck in-house compounds that were measured +for IC50 of inhibition on 99 protein kinases. Unlike most of +the other datasets featured in MoleculeNet, the Kinase +collection does not have structures for the compounds tested +since they were proprietary Merck compounds. However, the +collection does feature pre-computed descriptors for these +compounds. + +Note that the original train/valid/test split from the source +data was preserved here, so this function doesn't allow for +alternate modes of splitting. Similarly, since the source data +came pre-featurized, it is not possible to apply alternative +featurizations. + +This example features a few different models trained on this +dataset collection. In particular: + +- `kinase_rf.py` trains a random forest model diff --git a/examples/model_restore/README.md b/examples/model_restore/README.md new file mode 100644 index 0000000000..01c1eb6850 --- /dev/null +++ b/examples/model_restore/README.md @@ -0,0 +1,26 @@ +# Model Saving/Restoration + +In this example, we'll work through an example of using the +DeepChem API to save and restore a model from disk. We're going +to be training a ChemCeption model for this purpose on the +Delaney dataset. + +Here are the files we'll use + +- `chemception_model.py`: The file with the model to train +- `chemception_restore.py`: The file that restores the trained model + +To train the model, first run + +``` +python chemception_model.py +``` + +This will train a model and store it to a subdirectory `./model`. Let's now +invoke this model to make a prediction with it. + +``` +python chemception_restore.py +``` + +The scripts are pretty simple so go ahead and peek inside to see how they work. diff --git a/examples/model_restore/chemception_model.py b/examples/model_restore/chemception_model.py new file mode 100644 index 0000000000..ad0025b6fe --- /dev/null +++ b/examples/model_restore/chemception_model.py @@ -0,0 +1,35 @@ +""" +Script that trains Chemception models on delaney dataset. +""" +import numpy as np +np.random.seed(123) +import tensorflow as tf +tf.random.set_seed(123) +import deepchem as dc + +# Load Delaney dataset +delaney_tasks, delaney_datasets, transformers = dc.molnet.load_delaney( + featurizer='smiles2img', split='index', img_spec="engd") +train_dataset, valid_dataset, test_dataset = delaney_datasets + +# Get Metric +metric = dc.metrics.Metric(dc.metrics.pearson_r2_score, np.mean) + +model = dc.models.ChemCeption( + img_spec="engd", + n_tasks=len(delaney_tasks), + model_dir="./model", + mode="regression") + +# Fit trained model +model.fit(train_dataset, nb_epoch=1) + +print("Evaluating model") +train_scores = model.evaluate(train_dataset, [metric], transformers) +valid_scores = model.evaluate(valid_dataset, [metric], transformers) + +print("Train scores") +print(train_scores) + +print("Validation scores") +print(valid_scores) diff --git a/examples/model_restore/chemception_restore.py b/examples/model_restore/chemception_restore.py new file mode 100644 index 0000000000..9eb1f4f88d --- /dev/null +++ b/examples/model_restore/chemception_restore.py @@ -0,0 +1,16 @@ +import deepchem as dc +import rdkit.Chem as Chem + +model = dc.models.ChemCeption( + img_spec="engd", + n_tasks=1, + model_dir="./model", + mode="regression") +model.restore() + +smiles = "CCCCC" +featurizer = dc.feat.SmilesToImage(img_spec="engd", img_size=80, res=0.5) +dataset = dc.data.NumpyDataset(featurizer.featurize([Chem.MolFromSmiles(smiles)])) +prediction = model.predict(dataset) +print("smiles: %s" % smiles) +print("prediction: %s" % str(prediction)) diff --git a/examples/muv/__init__.py b/examples/muv/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/examples/nci/__init__.py b/examples/nci/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/examples/pcba/__init__.py b/examples/pcba/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/examples/pdbbind/__init__.py b/examples/pdbbind/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/examples/pretraining/README.md b/examples/pretraining/README.md new file mode 100644 index 0000000000..f22bf04585 --- /dev/null +++ b/examples/pretraining/README.md @@ -0,0 +1,13 @@ +# Pretraining Example + +In this example we will walk you through the use of pretraining +to transfer learned weights from a trained model to a new model. + +The code for transfering pretrained weights for a +fully-connected network is in `fnet_pretraining.py`. To run this +example, execute the following command in your shell + +``` +python fcnet_pretraining.py +``` + diff --git a/examples/pretraining/fcnet_pretraining.py b/examples/pretraining/fcnet_pretraining.py new file mode 100644 index 0000000000..e478a1e9ff --- /dev/null +++ b/examples/pretraining/fcnet_pretraining.py @@ -0,0 +1,52 @@ +import deepchem as dc +import numpy as np +import tensorflow as tf +from deepchem.models.losses import L2Loss +from tensorflow.keras.layers import Input, Dense + +class MLP(dc.models.KerasModel): + + def __init__(self, n_tasks=1, feature_dim=100, hidden_layer_size=64, + **kwargs): + self.feature_dim = feature_dim + self.hidden_layer_size = hidden_layer_size + self.n_tasks = n_tasks + + model, loss, output_types = self._build_graph() + super(MLP, self).__init__( + model=model, loss=loss, output_types=output_types, **kwargs) + + def _build_graph(self): + inputs = Input(dtype=tf.float32, shape=(self.feature_dim,), name="Input") + out1 = Dense(units=self.hidden_layer_size, activation='relu')(inputs) + + final = Dense(units=self.n_tasks, activation='sigmoid')(out1) + outputs = [final] + output_types = ['prediction'] + loss = dc.models.losses.BinaryCrossEntropy() + + model = tf.keras.Model(inputs=[inputs], outputs=outputs) + return model, loss, output_types + +X_1 = np.random.randn(100, 32) +y_1 = np.random.randn(100, 100) + +dataset_1 = dc.data.NumpyDataset(X_1, y_1) + +X_2 = np.random.randn(100, 32) +y_2 = np.random.randn(100, 10) + +dataset_2 = dc.data.NumpyDataset(X_2, y_2) + +source_model = MLP(feature_dim=32, hidden_layer_size=100, n_tasks=100) +source_model.fit(dataset_1, nb_epoch=100) + +dest_model = MLP(feature_dim=32, hidden_layer_size=100, n_tasks=10) +dest_model.load_from_pretrained( + source_model=source_model, + assignment_map=None, + value_map=None, + model_dir=None, + include_top=False) + +dest_model.fit(dataset_2, nb_epoch=100) diff --git a/examples/qm7/README.md b/examples/qm7/README.md new file mode 100644 index 0000000000..b678b99e83 --- /dev/null +++ b/examples/qm7/README.md @@ -0,0 +1,55 @@ +# QM7 Examples + +QM7 is a subset of GDB-13 (a database of nearly 1 billion +stable and synthetically accessible organic molecules) +containing up to 7 heavy atoms C, N, O, and S. The 3D +Cartesian coordinates of the most stable conformations and +their atomization energies were determined using ab-initio +density functional theory (PBE0/tier2 basis set).This dataset +also provided Coulomb matrices as calculated in [Rupp et al. +PRL, 2012]: + +- C_ii = 0.5 * Z^2.4 +- C_ij = Z_i * Z_j/abs(R_i − R_j) +- Z_i - nuclear charge of atom i +- R_i - cartesian coordinates of atom i + +The data file (.mat format, we recommend using `scipy.io.loadmat` for python users to load this original data) contains five arrays: +- "X" - (7165 x 23 x 23), Coulomb matrices +- "T" - (7165), atomization energies (unit: kcal/mol) +- "P" - (5 x 1433), cross-validation splits as used in [Montavon et al. NIPS, 2012] +- "Z" - (7165 x 23), atomic charges +- "R" - (7165 x 23 x 3), cartesian coordinate (unit: Bohr) of each atom in the molecules + +Reference: +Rupp, Matthias, et al. "Fast and accurate modeling of molecular atomization energies with machine learning." Physical review letters 108.5 (2012): 058301. +Montavon, Grégoire, et al. "Learning invariant representations of molecules for atomization energy prediction." Advances in Neural Information Processing Systems. 2012. + +# QM7B Examples + +QM7b is an extension for the QM7 dataset with additional +properties predicted at different levels (ZINDO, SCS, PBE0, GW). +In total 14 tasks are included for 7211 molecules with up to 7 +heavy atoms. + +The dataset in .mat format(for python users, we recommend using `scipy.io.loadmat`) includes two arrays: +- "X" - (7211 x 23 x 23), Coulomb matrices +- "T" - (7211 x 14), properties + Atomization energies E (PBE0, unit: kcal/mol) + Excitation of maximal optimal absorption E_max (ZINDO, unit: eV) + Absorption Intensity at maximal absorption I_max (ZINDO) + Highest occupied molecular orbital HOMO (ZINDO, unit: eV) + Lowest unoccupied molecular orbital LUMO (ZINDO, unit: eV) + First excitation energy E_1st (ZINDO, unit: eV) + Ionization potential IP (ZINDO, unit: eV) + Electron affinity EA (ZINDO, unit: eV) + Highest occupied molecular orbital HOMO (PBE0, unit: eV) + Lowest unoccupied molecular orbital LUMO (PBE0, unit: eV) + Highest occupied molecular orbital HOMO (GW, unit: eV) + Lowest unoccupied molecular orbital LUMO (GW, unit: eV) + Polarizabilities α (PBE0, unit: Å^3) + Polarizabilities α (SCS, unit: Å^3) + +Reference: +- Blum, Lorenz C., and Jean-Louis Reymond. "970 million druglike small molecules for virtual screening in the chemical universe database GDB-13." Journal of the American Chemical Society 131.25 (2009): 8732-8733. +- Montavon, Grégoire, et al. "Machine learning of molecular electronic properties in chemical compound space." New Journal of Physics 15.9 (2013): 095003. diff --git a/examples/qm7/__init__.py b/examples/qm7/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/examples/qm9/__init__.py b/examples/qm9/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/examples/sampl/__init__.py b/examples/sampl/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/examples/sider/__init__.py b/examples/sider/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/examples/splitters/README.md b/examples/splitters/README.md new file mode 100644 index 0000000000..defff01c87 --- /dev/null +++ b/examples/splitters/README.md @@ -0,0 +1,27 @@ +# Splitter Examples + +The DeepChem library has a collection of splitters which demonstrate +how to use DeepChem to split chemical and other datasets in +interesting ways. This folder contains a number of examples which +demonstrate the use of splitters on data + +DeepChem has a number of different splitters you can use on your data. Here's the current set + +``` +from deepchem.splits.splitters import RandomGroupSplitter +from deepchem.splits.splitters import RandomStratifiedSplitter +from deepchem.splits.splitters import SingletaskStratifiedSplitter +from deepchem.splits.splitters import MolecularWeightSplitter +from deepchem.splits.splitters import MaxMinSplitter +from deepchem.splits.splitters import RandomSplitter +from deepchem.splits.splitters import IndexSplitter +from deepchem.splits.splitters import IndiceSplitter +from deepchem.splits.splitters import ClusterFps +from deepchem.splits.splitters import ButinaSplitter +from deepchem.splits.splitters import ScaffoldSplitter +from deepchem.splits.splitters import FingerprintSplitter +from deepchem.splits.splitters import SpecifiedSplitter +from deepchem.splits.splitters import FingerprintSplitter +from deepchem.splits.splitters import TimeSplitterPDBbind +from deepchem.splits.task_splitter import TaskSplitter +``` diff --git a/examples/splitters/random_split.py b/examples/splitters/random_split.py new file mode 100644 index 0000000000..6209b67d3c --- /dev/null +++ b/examples/splitters/random_split.py @@ -0,0 +1,17 @@ +import deepchem as dc + +mols = ['C1=CC2=C(C=C1)C1=CC=CC=C21', 'O=C1C=CC(=O)C2=C1OC=CO2', 'C1=C[N]C=C1', 'C1=CC=CC=C[C+]1', 'C1=[C]NC=C1', 'N[C@@H](C)C(=O)O', 'N[C@H](C)C(=O)O', 'CC', 'O=C=O', 'C#N', 'CCN(CC)CC', 'CC(=O)O', 'C1CCCCC1', 'c1ccccc1'] +print("Original set of molecules") +print(mols) + +splitter = dc.splits.RandomSplitter(seed=123) +train, valid, test = splitter.train_valid_test_split(mols) +# The return values are dc.data.Dataset objects so we need to extract +# the ids +print("Training set") +print(train.ids) +print("Valid set") +print(valid.ids) +print("Test set") +print(test.ids) + diff --git a/examples/splitters/scaffold_split.py b/examples/splitters/scaffold_split.py new file mode 100644 index 0000000000..96b2f10bd0 --- /dev/null +++ b/examples/splitters/scaffold_split.py @@ -0,0 +1,17 @@ +import deepchem as dc + +mols = ['C1=CC2=C(C=C1)C1=CC=CC=C21', 'O=C1C=CC(=O)C2=C1OC=CO2', 'C1=C[N]C=C1', 'C1=CC=CC=C[C+]1', 'C1=[C]NC=C1', 'N[C@@H](C)C(=O)O', 'N[C@H](C)C(=O)O', 'CC', 'O=C=O', 'C#N', 'CCN(CC)CC', 'CC(=O)O', 'C1CCCCC1', 'c1ccccc1'] +print("Original set of molecules") +print(mols) + +splitter = dc.splits.ScaffoldSplitter(seed=123) +train, valid, test = splitter.train_valid_test_split(mols) +# The return values are dc.data.Dataset objects so we need to extract +# the ids +print("Training set") +print(train.ids) +print("Valid set") +print(valid.ids) +print("Test set") +print(test.ids) + diff --git a/examples/sweetlead/README.md b/examples/sweetlead/README.md new file mode 100644 index 0000000000..5108825789 --- /dev/null +++ b/examples/sweetlead/README.md @@ -0,0 +1,10 @@ +# Sweetlead example + +Sweetlead is a dataset of chemical structures for approved +drugs, chemical isolates from traditional medicinal herbs, and +regulated chemicals. Resulting structures are filtered for the +active pharmaceutical ingredient, standardized, and differing +formulations of the same drug were combined in the final +database. + +Novick, Paul A., et al. "SWEETLEAD: an in silico database of approved drugs, regulated chemicals, and herbal isolates for computer-aided drug discovery." PLoS One 8.11 (2013). diff --git a/examples/tests.py b/examples/tests.py deleted file mode 100644 index e5e6dd9164..0000000000 --- a/examples/tests.py +++ /dev/null @@ -1,39 +0,0 @@ -import os -import subprocess -import tempfile - - -def _example_run(path): - """Checks that the example at the specified path runs corrently. - - Parameters - ---------- - path: str - Path to example file. - Returns - ------- - result: int - Return code. 0 for success, failure otherwise. - """ - cmd = ["python", path] - # Will raise a CalledProcessError if fails. - retval = subprocess.check_output(cmd) - return retval - - -def test_adme(): - print("Running test_adme()") - output = _example_run("./adme/run_benchmarks.py") - print(output) - - -def test_tox21_fcnet(): - - print("Running tox21_fcnet()") - output = _example_run("./tox21/tox21_fcnet.py") - print(output) - - -if __name__ == "__main__": - test_tox21_fcnet() - test_adme() diff --git a/examples/tox21/__init__.py b/examples/tox21/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/examples/toxcast/README.md b/examples/toxcast/README.md new file mode 100644 index 0000000000..863af74fd2 --- /dev/null +++ b/examples/toxcast/README.md @@ -0,0 +1,19 @@ +# Toxcast Examples + +ToxCast is an extended data collection from the same +initiative as Tox21, providing toxicology data for a large +library of compounds based on in vitro high-throughput +screening. The processed collection includes qualitative +results of over 600 experiments on 8k compounds. + +The source data file contains a csv table, in which columns +below are used: + +- "smiles": SMILES representation of the molecular structure +- "ACEA_T47D_80hr_Negative" ~ "Tanguay_ZF_120hpf_YSE_up": Bioassays results. Please refer to the section "high-throughput assay information" at https://www.epa.gov/chemical-research/toxicity-forecaster-toxcasttm-data for details. + +The source paper is + +Richard, Ann M., et al. "ToxCast chemical landscape: paving the road to 21st century toxicology." Chemical research in toxicology 29.8 (2016): 1225-1251. + +In this example, we train a Random Forest model on the Toxcast dataset. diff --git a/examples/toxcast/__init__.py b/examples/toxcast/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/examples/transformers/README.md b/examples/transformers/README.md new file mode 100644 index 0000000000..aa32c69f9a --- /dev/null +++ b/examples/transformers/README.md @@ -0,0 +1,17 @@ +# Transformer Examples + +In this example directory, we provide usage examples for the +transformers DeepChem supports. Here's a list of the transformers DeepChem +currently supports: + +- `LogTransformer` +- `ClippingTransformer` +- `NormalizationTransformer` +- `BalancingTransformer` +- `CDFTransformer` +- `PowerTransformer` +- `CoulombFitTransformer` +- `IRVTransformer` +- `DAGTransformer` +- `ANITransformer` +- `MinMaxTransformer` diff --git a/examples/uv/README.md b/examples/uv/README.md new file mode 100644 index 0000000000..ad092e2eef --- /dev/null +++ b/examples/uv/README.md @@ -0,0 +1,19 @@ +# UV Examples + +The UV dataset is an in-house dataset from Merck that was first introduced in the following paper: + +Ramsundar, Bharath, et al. "Is multitask deep learning practical for pharma?." Journal of chemical information and modeling 57.8 (2017): 2068-2076. + +The UV dataset tests 10,000 of Merck's internal compounds on +190 absorption wavelengths between 210 and 400 nm. Unlike +most of the other datasets featured in MoleculeNet, the UV +collection does not have structures for the compounds tested +since they were proprietary Merck compounds. However, the +collection does feature pre-computed descriptors for these +compounds. + +Note that the original train/valid/test split from the source +data was preserved here, so this function doesn't allow for +alternate modes of splitting. Similarly, since the source data +came pre-featurized, it is not possible to apply alternative +featurizations.