Accurate prediction of drug-target interactions (DTI) enables drug discoverytasks, including virtual screening and drug repurposing, which can shorten the time to identify promising drug candidates and provide cures to patients . Recently, there is a growing number of research that developed deep learning (DL) models for DTI. Despite their superior performance, these research models are difficult to use in real drug discovery practice due to the complexity of deploying the research code as well as the restricted data formatting, model capacity, and evaluation setting . We present DeepPurpose, a comprehensive and easy-to-use software toolkit for DL based drug-target interaction (DTI ) predictionwith applications to drug screening and repurposing. The unique feature of DeepPurpose is that it enables non-computational drug development scientists to identify drug candidates based on five pre-trained DL models with only a few lines of codes. Further, computer scientists can use DeepPurpose to train customized DTI prediction models with 15 drug and target encodings and 50 + novel DL architectures. To tackle method development challenges, DeepPurpose also supports various data split settings and preloads five benchmarking datasets. We demonstrated that DeepPurpose allows users to obtain state-of-the-art prediction performance on several benchmark datasets . We also presented several case studies, including a study on drug repurposing for COVID-19, where promising drug candidates currently investigated in clinical trials are ranked high in DeepPurpose predictions .
Accurate prediction of drug-target interactions (DTI) is crucial for drug discovery . Recently, deep learning (DL) models for show promising performance for DTI prediction. However, these models can be difficult to use for both computer scientists entering the biomedical field and bioinformaticians with limited DL experience . We present DeepPurpose, a comprehensive and easy-to-use deep learning library for DTI prediction. DeepPurpose supports training of customized DTI prediction models by implementing 15 compound and protein encoders and over 50 neural architectures, along with providing many other useful features. We demonstrate state-of-the-art performance of DeepPurpose on several benchmark datasets .
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enables drug discoverytasks, including virtual screening and drug repurposing, which can shorten the time to identify promising drug candidates and provide cures to patients
is crucial for drug discovery
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there is a growing number of research that developed
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DTI. Despite their superior performance, these research models are
show promising performance for DTI prediction. However, these models can be
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in real drug discovery practice due to the complexity of deploying the research code as well as the restricted data formatting, model capacity, and evaluation setting
for both computer scientists entering the biomedical field and bioinformaticians with limited DL experience
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software toolkit for DL based drug-target interaction (DTI ) predictionwith applications to drug screening and repurposing. The unique feature of DeepPurpose is that it enables non-computational drug development scientists to identify drug candidates based on five pre-trained DL models with only a few lines of codes. Further, computer scientists can use DeepPurpose to train
deep learning library for DTI prediction. DeepPurpose supports training of
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with
by implementing
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drug and target encodings and
compound and protein encoders and over
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+ novel DL architectures. To tackle method development challenges, DeepPurpose also supports various data split settings and preloads five benchmarking datasets. We demonstrated that DeepPurpose allows users to obtain
neural architectures, along with providing many other useful features. We demonstrate
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prediction performance
performance of DeepPurpose
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. We also presented several case studies, including a study on drug repurposing for COVID-19, where promising drug candidates currently investigated in clinical trials are ranked high in DeepPurpose predictions