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+What is drug repurposing, virtual screening and drug-target interaction prediction?
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+Drug Repurposing
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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+Drug repurposing aims to repivot an existing drug to a new therapy.
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+Virtual Screening
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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+Virtual screening means to use computer software to automatically screen
+ a huge space of potential drug-target pairs to obtain a predicted binding score. 
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+Drug-Target Interaction
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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+Both of these tasks are able to save cost, time, and facilitate drug discovery. 
+Deep learning has shown strong performance in repurposing and screening. 
+It relies on the accurate and fast prediction of a fundamental task: 
+drug-target interaction prediction. 
+DTI prediction task aims to predict the input drug target pair’s interaction probability or binding score. 
+Given a powerful DTI model that is able to generalize over a new unseen dataset, 
+we can then extend to repurposing/screening. 
+For repurposing, given a new target of interest, 
+we can first pair it to a repurposing drug library. 
+Then this list of input drug-target pairs is fed into the trained DTI model, 
+which will output the predicted binding score. 
+Similarly, for virtual screening, given a list of screening drug-target pairs we want, 
+the DTI model can output the predicted interaction binding scores. 
+We can then rank the predicted outcome based on their binding scores and 
+test the top-k options in the wet lab after manual inspection. 
+DeepPurpose automates this process. By only requiring one line of code, 
+it aggregates five pretrained deep learning models and retrieves a list of ranked potential outcomes. 
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+Identifying Drug-Target Interactions (DTI) will greatly narrow down
+the scope of search of candidate medications, 
+and thus can plays a pivotal role in drug discovery. 
+Drugs usually interact with one or more proteins to achieve their functions. 
+However, discovering novel interactions between drugs 
+and target proteins is crucial for the development of new drugs, 
+since the aberrant expression of proteins may cause side effects of drugs. 
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+Considering that in vitro experiments are extremely costly and time-consuming, 
+high efficiency computational prediction methods could 
+serve as promising strategies for drug-target interaction (DTI) prediction. 
+In this project, our goal is to focus on deep learning approaches 
+for drug-target interaction (DTI) prediction. 
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