DeepPurpose 0.1.5

a Deep Learning Based Toolkit for Drug Discovery

A Deep Learning Library for Compound and Protein Modeling
DTI, Drug Property, PPI, DDI, Protein Function Prediction

Applications in Drug Repurposing, Virtual Screening, QSAR, Side Effect Prediction and More

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This repository hosts DeepPurpose, a Deep Learning Based Molecular Modeling and Prediction Toolkit on Drug-Target Interaction Prediction, Compound Property Prediction, Protein-Protein Interaction Prediction, and Protein Function prediction (using PyTorch). We focus on DTI and its applications in Drug Repurposing and Virtual Screening, but support various other molecular encoding tasks. It allows very easy usage (several lines of codes only) to facilitate deep learning for life science research.

News!

• [05/21] 0.1.2 Support 5 new graph neural network based models for compound encoding (DGL_GCN, DGL_NeuralFP, DGL_GIN_AttrMasking, DGL_GIN_ContextPred, DGL_AttentiveFP), implemented using DGL Life Science! An example is provided here!
• [12/20] DeepPurpose is now supported by TDC data loader, which contains a large collection of ML for therapeutics datasets, including many drug property, DTI datasets. Here is a tutorial!
• [12/20] DeepPurpose can now be installed via pip!
• [11/20] DeepPurpose is published in Bioinformatics!
• [11/20] Added 5 more pretrained models on BindingDB IC50 Units (around 1Million data points).
• [10/20] Google Colab Installation Instructions are provided here. Thanks to @hima111997 !
• [10/20] Using DeepPurpose, we made a humans-in-the-loop molecular design web UI interface, check it out! [Website, paper]
• [09/20] DeepPurpose has now supported three more tasks: DDI, PPI and Protein Function Prediction! You can simply call from DeepPurpose import DDI/PPI/ProteinPred to use, checkout examples below!
• [07/20] A simple web UI for DTI prediction can be created under 10 lines using Gradio! A demo is provided here.
• [07/20] A blog is posted on the Towards Data Science Medium column, check this out!
• [07/20] Two tutorials are online to go through DeepPurpose's framework to do drug-target interaction prediction and drug property prediction (DTI, Drug Property).
• [05/20] Support drug property prediction for screening data that does not have target proteins such as bacteria! An example using RDKit2D with DNN for training and repurposing for pseudomonas aeruginosa (MIT AI Cures's open task) is provided as a demo.
• [05/20] Now supports hyperparameter tuning via Bayesian Optimization through the Ax platform! A demo is provided in here.

Features

• 15+ powerful encodings for drugs and proteins, ranging from deep neural network on classic cheminformatics fingerprints, CNN, transformers to message passing graph neural network, with 50+ combined models! Most of the combinations of the encodings are not yet in existing works. All of these under 10 lines but with lots of flexibility! Switching encoding is as simple as changing the encoding names!

• Realistic and user-friendly design:

  • support DTI, DDI, PPI, molecular property prediction, protein function predictions!
  • automatic identification to do drug target binding affinity (regression) or drug target interaction prediction (binary) task.
  • support cold target, cold drug settings for robust model evaluations and support single-target high throughput sequencing assay data setup.
  • many dataset loading/downloading/unzipping scripts to ease the tedious preprocessing, including antiviral, COVID19 targets, BindingDB, DAVIS, KIBA, ...
  • many pretrained checkpoints.
  • easy monitoring of training process with detailed training metrics output such as test set figures (AUCs) and tables, also support early stopping.
  • detailed output records such as rank list for repurposing result.
  • various evaluation metrics: ROC-AUC, PR-AUC, F1 for binary task, MSE, R-squared, Concordance Index for regression task.
  • label unit conversion for skewed label distribution such as Kd.
  • time reference for computational expensive encoding.
  • PyTorch based, support CPU, GPU, Multi-GPUs.

NOTE: We are actively looking for constructive advices/user feedbacks/experiences on using DeepPurpose! Please open an issue or contact us.

Cite Us

If you found this package useful, please cite our paper:

@article{huang2020deeppurpose,
  title={DeepPurpose: A Deep Learning Library for Drug-Target Interaction Prediction},
  author={Huang, Kexin and Fu, Tianfan and Glass, Lucas M and Zitnik, Marinka and Xiao, Cao and Sun, Jimeng},
  journal={Bioinformatics},
  year={2020}
}

Installation

Try it on Binder! Binder is a cloud Jupyter Notebook interface that will install our environment dependency for you.

Video tutorial to install Binder.

We recommend to install it locally since Binder needs to be refreshed every time launching. To install locally, we recommend to install from pip:

pip

conda create -n DeepPurpose python=3.6
conda activate DeepPurpose
conda install -c conda-forge rdkit
conda install -c conda-forge notebook
pip install git+https://github.com/bp-kelley/descriptastorus 
pip install DeepPurpose

Build from Source

First time:

git clone https://github.com/kexinhuang12345/DeepPurpose.git ## Download code repository
cd DeepPurpose ## Change directory to DeepPurpose
conda env create -f environment.yml  ## Build virtual environment with all packages installed using conda
conda activate DeepPurpose ## Activate conda environment (use "source activate DeepPurpose" for anaconda 4.4 or earlier) 
jupyter notebook ## open the jupyter notebook with the conda env

## run our code, e.g. click a file in the DEMO folder
... ...

conda deactivate ## when done, exit conda environment 

In the future:

cd DeepPurpose ## Change directory to DeepPurpose
conda activate DeepPurpose ## Activate conda environment
jupyter notebook ## open the jupyter notebook with the conda env

## run our code, e.g. click a file in the DEMO folder
... ...

conda deactivate ## when done, exit conda environment 

Video tutorial to install locally from source.

Example

Case Study 1(a): A Framework for Drug Target Interaction Prediction, with less than 10 lines of codes.

In addition to the DTI prediction, we also provide repurpose and virtual screening functions to rapidly generation predictions.

Click here for the code!

from DeepPurpose import DTI as models
from DeepPurpose.utils import *
from DeepPurpose.dataset import *

# Load Data, an array of SMILES for drug, an array of Amino Acid Sequence for Target and an array of binding values/0-1 label.
# e.g. ['Cc1ccc(CNS(=O)(=O)c2ccc(s2)S(N)(=O)=O)cc1', ...], ['MSHHWGYGKHNGPEHWHKDFPIAKGERQSPVDIDTH...', ...], [0.46, 0.49, ...]
# In this example, BindingDB with Kd binding score is used.
X_drug, X_target, y  = process_BindingDB(download_BindingDB(SAVE_PATH),
					 y = 'Kd', 
					 binary = False, 
					 convert_to_log = True)

# Type in the encoding names for drug/protein.
drug_encoding, target_encoding = 'MPNN', 'Transformer'

# Data processing, here we select cold protein split setup.
train, val, test = data_process(X_drug, X_target, y, 
                                drug_encoding, target_encoding, 
                                split_method='cold_protein', 
                                frac=[0.7,0.1,0.2])

# Generate new model using default parameters; also allow model tuning via input parameters.
config = generate_config(drug_encoding, target_encoding, transformer_n_layer_target = 8)
net = models.model_initialize(**config)

# Train the new model.
# Detailed output including a tidy table storing validation loss, metrics, AUC curves figures and etc. are stored in the ./result folder.
net.train(train, val, test)

# or simply load pretrained model from a model directory path or reproduced model name such as DeepDTA
net = models.model_pretrained(MODEL_PATH_DIR or MODEL_NAME)

# Repurpose using the trained model or pre-trained model
# In this example, loading repurposing dataset using Broad Repurposing Hub and SARS-CoV 3CL Protease Target.
X_repurpose, drug_name, drug_cid = load_broad_repurposing_hub(SAVE_PATH)
target, target_name = load_SARS_CoV_Protease_3CL()

_ = models.repurpose(X_repurpose, target, net, drug_name, target_name)

# Virtual screening using the trained model or pre-trained model 
X_repurpose, drug_name, target, target_name = ['CCCCCCCOc1cccc(c1)C([O-])=O', ...], ['16007391', ...], ['MLARRKPVLPALTINPTIAEGPSPTSEGASEANLVDLQKKLEEL...', ...], ['P36896', 'P00374']

_ = models.virtual_screening(X_repurpose, target, net, drug_name, target_name)

Case Study 1(b): A Framework for Drug Property Prediction, with less than 10 lines of codes.

Many dataset is in the form of high throughput screening data, which have only drug and its activity score. It can be formulated as a drug property prediction task. We also provide a repurpose function to predict over large space of drugs.

Click here for the code!

from DeepPurpose import CompoundPred as models
from DeepPurpose.utils import *
from DeepPurpose.dataset import *

# load AID1706 Assay Data
X_drugs, _, y = load_AID1706_SARS_CoV_3CL()

drug_encoding = 'rdkit_2d_normalized'
train, val, test = data_process(X_drug = X_drugs, y = y, 
			    drug_encoding = drug_encoding,
			    split_method='random', 
			    random_seed = 1)

config = generate_config(drug_encoding = drug_encoding, 
                         cls_hidden_dims = [512], 
                         train_epoch = 20, 
                         LR = 0.001, 
                         batch_size = 128,
                        )
model = models.model_initialize(**config)
model.train(train, val, test)

X_repurpose, drug_name, drug_cid = load_broad_repurposing_hub(SAVE_PATH)

_ = models.repurpose(X_repurpose, model, drug_name)

Case Study 1(c): A Framework for Drug-Drug Interaction Prediction, with less than 10 lines of codes.

DDI is very important for drug safety profiling and the success of clinical trials. This framework predicts interaction based on drug pairs chemical structure.

Click here for the code!

from DeepPurpose import DDI as models
from DeepPurpose.utils import *
from DeepPurpose.dataset import *

# load DB Binary Data
X_drugs, X_drugs_, y = read_file_training_dataset_drug_drug_pairs()

drug_encoding = 'rdkit_2d_normalized'
train, val, test = data_process(X_drug = X_drugs, X_drug_ = X_drugs_, y = y, 
			    drug_encoding = drug_encoding,
			    split_method='random', 
			    random_seed = 1)

config = generate_config(drug_encoding = drug_encoding, 
                         cls_hidden_dims = [512], 
                         train_epoch = 20, 
                         LR = 0.001, 
                         batch_size = 128,
                        )

model = models.model_initialize(**config)
model.train(train, val, test)

Case Study 1(d): A Framework for Protein-Protein Interaction Prediction, with less than 10 lines of codes.

PPI is important to study the relations among targets.

Click here for the code!

from DeepPurpose import PPI as models
from DeepPurpose.utils import *
from DeepPurpose.dataset import *

# load DB Binary Data
X_targets, X_targets_, y = read_file_training_dataset_protein_proteins_pairs()

target_encoding = 'CNN'
train, val, test = data_process(X_target = X_targets, X_target_ = X_targets_, y = y, 
			    target_encoding = target_encoding,
			    split_method='random', 
			    random_seed = 1)

config = generate_config(target_encoding = target_encoding, 
                         cls_hidden_dims = [512], 
                         train_epoch = 20, 
                         LR = 0.001, 
                         batch_size = 128,
                        )

model = models.model_initialize(**config)
model.train(train, val, test)

Case Study 1(e): A Framework for Protein Function Prediction, with less than 10 lines of codes.

Protein function prediction help predict various useful functions such as GO terms, structural classification and etc. Also, for biologics drugs, it is also useful for screening.

Click here for the code!

from DeepPurpose import ProteinPred as models
from DeepPurpose.utils import *
from DeepPurpose.dataset import *

# load DB Binary Data
X_targets, y = read_file_protein_function()

target_encoding = 'CNN'
train, val, test = data_process(X_target = X_targets, y = y, 
			    target_encoding = target_encoding,
			    split_method='random', 
			    random_seed = 1)

config = generate_config(target_encoding = target_encoding, 
                         cls_hidden_dims = [512], 
                         train_epoch = 20, 
                         LR = 0.001, 
                         batch_size = 128,
                        )

model = models.model_initialize(**config)
model.train(train, val, test)

Case Study 2 (a): Antiviral Drugs Repurposing for SARS-CoV2 3CLPro, using One Line.

Given a new target sequence (e.g. SARS-CoV2 3CL Protease), retrieve a list of repurposing drugs from a curated drug library of 81 antiviral drugs. The Binding Score is the Kd values. Results aggregated from five pretrained model on BindingDB dataset! (Caution: this currently is for educational purposes. The pretrained DTI models only cover a small dataset and thus cannot generalize to every new unseen protein. For best use case, train your own model with customized data.)

Click here for the code!

from DeepPurpose import oneliner
from DeepPurpose.dataset import *
oneliner.repurpose(*load_SARS_CoV2_Protease_3CL(), *load_antiviral_drugs(no_cid = True))

----output----
Drug Repurposing Result for SARS-CoV2 3CL Protease
+------+----------------------+------------------------+---------------+
| Rank |      Drug Name       |      Target Name       | Binding Score |
+------+----------------------+------------------------+---------------+
|  1   |      Sofosbuvir      | SARS-CoV2 3CL Protease |     190.25    |
|  2   |     Daclatasvir      | SARS-CoV2 3CL Protease |     214.58    |
|  3   |      Vicriviroc      | SARS-CoV2 3CL Protease |     315.70    |
|  4   |      Simeprevir      | SARS-CoV2 3CL Protease |     396.53    |
|  5   |      Etravirine      | SARS-CoV2 3CL Protease |     409.34    |
|  6   |      Amantadine      | SARS-CoV2 3CL Protease |     419.76    |
|  7   |      Letermovir      | SARS-CoV2 3CL Protease |     460.28    |
|  8   |     Rilpivirine      | SARS-CoV2 3CL Protease |     470.79    |
|  9   |      Darunavir       | SARS-CoV2 3CL Protease |     472.24    |
|  10  |      Lopinavir       | SARS-CoV2 3CL Protease |     473.01    |
|  11  |      Maraviroc       | SARS-CoV2 3CL Protease |     474.86    |
|  12  |    Fosamprenavir     | SARS-CoV2 3CL Protease |     487.45    |
|  13  |      Ritonavir       | SARS-CoV2 3CL Protease |     492.19    |
....

Case Study 2(b): Repurposing using Customized training data, with One Line.

Given a new target sequence (e.g. SARS-CoV 3CL Pro), training on new data (AID1706 Bioassay), and then retrieve a list of repurposing drugs from a proprietary library (e.g. antiviral drugs). The model can be trained from scratch or finetuned from the pretraining checkpoint!

Click here for the code!

from DeepPurpose import oneliner
from DeepPurpose.dataset import *

oneliner.repurpose(*load_SARS_CoV_Protease_3CL(), *load_antiviral_drugs(no_cid = True),  *load_AID1706_SARS_CoV_3CL(), \
		split='HTS', convert_y = False, frac=[0.8,0.1,0.1], pretrained = False, agg = 'max_effect')

----output----
Drug Repurposing Result for SARS-CoV 3CL Protease
+------+----------------------+-----------------------+-------------+-------------+
| Rank |      Drug Name       |      Target Name      | Interaction | Probability |
+------+----------------------+-----------------------+-------------+-------------+
|  1   |      Remdesivir      | SARS-CoV 3CL Protease |     YES     |     0.99    |
|  2   |      Efavirenz       | SARS-CoV 3CL Protease |     YES     |     0.98    |
|  3   |      Vicriviroc      | SARS-CoV 3CL Protease |     YES     |     0.98    |
|  4   |      Tipranavir      | SARS-CoV 3CL Protease |     YES     |     0.96    |
|  5   |     Methisazone      | SARS-CoV 3CL Protease |     YES     |     0.94    |
|  6   |      Letermovir      | SARS-CoV 3CL Protease |     YES     |     0.88    |
|  7   |     Idoxuridine      | SARS-CoV 3CL Protease |     YES     |     0.77    |
|  8   |       Loviride       | SARS-CoV 3CL Protease |     YES     |     0.76    |
|  9   |      Baloxavir       | SARS-CoV 3CL Protease |     YES     |     0.74    |
|  10  |     Ibacitabine      | SARS-CoV 3CL Protease |     YES     |     0.70    |
|  11  |     Taribavirin      | SARS-CoV 3CL Protease |     YES     |     0.65    |
|  12  |      Indinavir       | SARS-CoV 3CL Protease |     YES     |     0.62    |
|  13  |   Podophyllotoxin    | SARS-CoV 3CL Protease |     YES     |     0.60    |
....

Demos

Checkout 10+ demos & tutorials to start:

Name	Description
Dataset Tutorial	Tutorial on how to use the dataset loader and read customized data
Drug Repurposing for 3CLPro	Example of one-liner repurposing for 3CLPro
Drug Repurposing with Customized Data	Example of one-liner repurposing with AID1706 Bioassay Data, training from scratch
Virtual Screening for BindingDB IC50	Example of one-liner virtual screening
Reproduce DeepDTA	Reproduce DeepDTA with DAVIS dataset and show how to use the 10 lines framework
Virtual Screening for DAVIS and Correlation Plot	Example of one-liner virtual screening and evaluate on unseen dataset by plotting correlation
Binary Classification for DAVIS using CNNs	Binary Classification for DAVIS dataset using CNN encodings by using the 10 lines framework.
Pretraining Model Tutorial	Tutorial on how to load pretraining models

and more in the DEMO folder!

Contact

Please contact [e-mail] or [e-mail] for help or submit an issue.

Encodings

Currently, we support the following encodings:

Drug Encodings	Description
Morgan	Extended-Connectivity Fingerprints
Pubchem	Pubchem Substructure-based Fingerprints
Daylight	Daylight-type fingerprints
rdkit_2d_normalized	Normalized Descriptastorus
ESPF	Explainable Substructure Partition Fingerprint
ErG	2D pharmacophore descriptions for scaffold hopping
CNN	Convolutional Neural Network on SMILES
CNN_RNN	A GRU/LSTM on top of a CNN on SMILES
Transformer	Transformer Encoder on ESPF
MPNN	Message-passing neural network
DGL_GCN	Graph Convolutional Network
DGL_NeuralFP	Neural Fingerprint
DGL_GIN_AttrMasking	Pretrained GIN with Attribute Masking
DGL_GIN_ContextPred	Pretrained GIN with Context Prediction
DGL_AttentiveFP	Attentive FP, Xiong et al. 2020

Target Encodings	Description
AAC	Amino acid composition up to 3-mers
PseudoAAC	Pseudo amino acid composition
Conjoint_triad	Conjoint triad features
Quasi-seq	Quasi-sequence order descriptor
ESPF	Explainable Substructure Partition Fingerprint
CNN	Convolutional Neural Network on target seq
CNN_RNN	A GRU/LSTM on top of a CNN on target seq
Transformer	Transformer Encoder on ESPF

Data

DeepPurpose supports the following dataset loaders for now and more will be added:

Public Drug-Target Binding Benchmark Dataset

Data	Function
BindingDB	download_BindingDB() to download the data and process_BindingDB() to process the data
DAVIS	load_process_DAVIS() to download and process the data
KIBA	load_process_KIBA() to download and process the data

Repurposing Dataset

Data	Function
Curated Antiviral Drugs Library	load_antiviral_drugs() to load and process the data
Broad Repurposing Hub	load_broad_repurposing_hub() downloads and process the data

Bioassay Data for COVID-19 (Thanks to MIT AI Cures)

Data	Function
AID1706	load_AID1706_SARS_CoV_3CL() to load and process

COVID-19 Targets

Data	Function
SARS-CoV 3CL Protease	load_SARS_CoV_Protease_3CL()
SARS-CoV2 3CL Protease	load_SARS_CoV2_Protease_3CL()
SARS_CoV2 RNA Polymerase	load_SARS_CoV2_RNA_polymerase()
SARS-CoV2 Helicase	load_SARS_CoV2_Helicase()
SARS-CoV2 3to5_exonuclease	load_SARS_CoV2_3to5_exonuclease()
SARS-CoV2 endoRNAse	load_SARS_CoV2_endoRNAse()

DeepPurpose also supports to read from users' txt file. It assumes the following data format.

Click here for the format expected!

For drug target pairs:

Drug1_SMILES Target1_Seq Score/Label
Drug2_SMILES Target2_Seq Score/Label
....

Then, use

from DeepPurpose import dataset
X_drug, X_target, y = dataset.read_file_training_dataset_drug_target_pairs(PATH)

For bioassay training data:

Target_Seq
Drug1_SMILES Score/Label
Drug2_SMILES Score/Label
....

Then, use

from DeepPurpose import dataset
X_drug, X_target, y = dataset.read_file_training_dataset_bioassay(PATH)

For drug property prediction training data:

Drug1_SMILES Score/Label
Drug2_SMILES Score/Label
....

Then, use

from DeepPurpose import dataset
X_drug, y = dataset.read_file_compound_property(PATH)

For protein function prediction training data:

Target1_Seq Score/Label
Target2_Seq Score/Label
....

Then, use

from DeepPurpose import dataset
X_drug, y = dataset.read_file_protein_function(PATH)

For drug drug pairs:

Drug1_SMILES Drug1_SMILES_ Score/Label
Drug2_SMILES Drug2_SMILES_ Score/Label
....

Then, use

from DeepPurpose import dataset
X_drug, X_target, y = dataset.read_file_training_dataset_drug_drug_pairs(PATH)

For protein protein pairs:

Target1_Seq Target1_Seq_ Score/Label
Target2_Seq Target2_Seq_ Score/Label
....

Then, use

from DeepPurpose import dataset
X_drug, X_target, y = dataset.read_file_training_dataset_protein_proteins_pairs(PATH)

For drug repurposing library:

Drug1_Name Drug1_SMILES 
Drug2_Name Drug2_SMILES
....

Then, use

from DeepPurpose import dataset
X_drug, X_drug_names = dataset.read_file_repurposing_library(PATH)

For target sequence to be repurposed:

Target_Name Target_seq 

Then, use

from DeepPurpose import dataset
Target_seq, Target_name = dataset.read_file_target_sequence(PATH)

For virtual screening library:

Drug1_SMILES Drug1_Name Target1_Seq Target1_Name
Drug1_SMILES Drug1_Name Target1_Seq Target1_Name
....

Then, use

from DeepPurpose import dataset
X_drug, X_target, X_drug_names, X_target_names = dataset.read_file_virtual_screening_drug_target_pairs(PATH)

Checkout Dataset Tutorial.

Pretrained models

We provide more than 10 pretrained models. Please see Pretraining Model Tutorial on how to load them. It is as simple as

from DeepPurpose import DTI as models
net = models.model_pretrained(model = 'MPNN_CNN_DAVIS')
or
net = models.model_pretrained(FILE_PATH)

The list of avaiable pretrained models:

Model name consists of first the drug encoding, then the target encoding and then the trained dataset.

Note that for DTI models, the BindingDB and DAVIS are trained on log scale. But DeepPurpose allows you to specify conversion between log scale (e.g. pIC50) and original scale by the variable convert_y.

Click here for the models supported!

Model Name
CNN_CNN_BindingDB_IC50
Morgan_CNN_BindingDB_IC50
Morgan_AAC_BindingDB_IC50
MPNN_CNN_BindingDB_IC50
Daylight_AAC_BindingDB_IC50
CNN_CNN_DAVIS
CNN_CNN_BindingDB
Morgan_CNN_BindingDB
Morgan_CNN_KIBA
Morgan_CNN_DAVIS
MPNN_CNN_BindingDB
MPNN_CNN_KIBA
MPNN_CNN_DAVIS
Transformer_CNN_BindingDB
Daylight_AAC_DAVIS
Daylight_AAC_KIBA
Daylight_AAC_BindingDB
Morgan_AAC_BindingDB
Morgan_AAC_KIBA
Morgan_AAC_DAVIS

Documentations

https://deeppurpose.readthedocs.io is under active development.

Disclaimer

The output list should be inspected manually by experts before proceeding to the wet-lab validation, and our work is still in active developement with limitations, please do not directly use the drugs.