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+Case Study  
+================================================
+
+* **1a. 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!
+
+.. code-block:: python
+
+
+	from DeepPurpose import oneliner
+	oneliner.repurpose(*load_SARS_CoV2_Protease_3CL(), *load_antiviral_drugs())
+
+
+
+
+* **1b. New Target Repurposing using Broad Drug Repurposing Hub, with One Line.**
+
+
+Given a new target sequence (e.g. MMP9), 
+retrieve a list of repurposing drugs from Broad Drug Repurposing Hub, 
+which is the default. 
+Results also aggregated from five pretrained model! 
+Note the drug name here is the Pubchem CID since some drug names in Broad is too long.
+
+.. code-block:: python
+
+	from DeepPurpose import oneliner
+	oneliner.repurpose(*load_MMP9())
+
+
+
+
+* **2. 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!
+
+
+
+.. code-block:: python
+
+
+	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')
+
+
+
+
+
+
+
+
+
+* 3. **A Framework for Drug Target Interaction Prediction, with less than 10 lines of codes.**
+
+Under the hood of one model from scratch, a flexible framework for method researchers:
+
+.. code-block:: python
+
+
+	from DeepPurpose import 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)
+
+
+
+
+
+
+* 4. **Virtual Screening with Customized Training Data with One Line**
+
+Given a list of new drug-target pairs to be screened, 
+retrieve a list of drug-target pairs with top predicted binding scores. 
+
+
+.. code-block:: python
+
+	from DeepPurpose import oneliner
+	oneliner.virtual_screening(['MKK...LIDL', ...], ['CC1=C...C4)N', ...])
+
+
+
+
+
+
+
+