DeepPurpose: a deep learning library for drug–target interaction prediction

Huang, Kexin; Fu, Tianfan; Glass, Lucas M.; Žitnik, Marinka; Xiao, Cao; Sun, Jimeng

doi:10.1093/bioinformatics/btaa1005

Cited by 257 publications

(239 citation statements)

References 15 publications

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“… Author Study keywords Targets No. of drugs Reference Ke, Y. Y Deep neural network FIP 80 Ke et al (2020) Ge, Y Knowledge Graph, deep learning SARS-CoV-2 64 Ge et al (2020) Beck, B. R Hybrid CNN and RNN model called MT-DTI 3CLpro, RdRp, helicase, 3′-to-5′ exonuclease, endoRNAse, and 2′-O-ribose methyltransferase 3410 Beck et al (2020) Zeng, X Deep learning-based knowledge graph (CoV-KGE), SARS-CoV-2 41 Zeng et al (2020) Gao, K 2-D fingerprint, GBDT model, Recurrent Neural Network (RNN) 3CLpro 40 Gao et al (2020) Hofmarcher, M Deep neural network, ChemAI 3CLpro, PLP 20 Hofmarcher et al (2020) Ton, A. T deep learning platform – Deep Docking (DD) Mpro 1000 Ton et al (2020) Hu, F Deep learning-based multi-task models, Classification and Regression 3CLpro 10 Hu and Jiang (2020) Gysi, D.M Graph Neural Network SARS-CoV-2 77 Gysi et al (2020) Huang, K Deep Purpose, Python toolkit, CNN 3CLpro 13 Huang et al (2020b) Batra, R Random forest (RF) regression algorithm, ensemble docking S, S-ACE2 complex 187 Batra et al (2020) Redka, D.S Deep learning, Ligand Design, MatchMaker, PolypharmDB 3CLpro, Spike; ACE2, TMPRSS2, Cathepsin B 30 Redka...…”

Section: Resultsmentioning

confidence: 99%

“…They developed a multimodal approach that has combinations of different algorithms and further identified 77 potential repurposing drugs. Huang and colleagues ( Huang et al, 2020b ) developed a Python-based DL toolkit, DeepPurpose that is based on an encoder-decoder framework and presented a case study on SARS-CoV-2 3CL pro with 13 potential repurposing candidates identified. Batra et al (2020) trained and validated a random forest algorithm on data from Smith et al ( Smith and Smith, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2

Prasad

Kumar

2021

Current Research in Pharmacology and Drug Discovery

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It has been said that COVID-19 is a generational challenge in many ways. But, at the same time, it becomes a catalyst for collective action, innovation, and discovery. Realizing the full potential of artificial intelligence (AI) for structure determination of unknown proteins and drug discovery are some of these innovations. Potential applications of AI include predicting the structure of the infectious proteins, identifying drugs that may be effective in targeting these proteins, and proposing new chemical compounds for further testing as potential drugs. AI and machine learning (ML) allow for rapid drug development including repurposing existing drugs. Algorithms were used to search for novel or approved antiviral drugs capable of inhibiting SARS-CoV-2. This paper presents a survey of AI and ML methods being used in various biochemistry of SARS-CoV-2, from structure to drug development, in the fight against the deadly COVID-19 pandemic. It is envisioned that this study will provide AI/ML researchers and the wider community an overview of the current status of AI applications particularly in structural biology, drug repurposing, and development, and motivate researchers in harnessing AI potentials in the fight against COVID-19.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2

Prasad

Kumar

2021

Current Research in Pharmacology and Drug Discovery

View full text Add to dashboard Cite

show abstract

“…Also, estimation of matrix of similarity limits the number of molecules. A DTI model which used DL, DeepDTA (K. Huang et al, 2021), was introduced to improve the above models. It used a CNN based model that waives requirements related to feature engineering.…”

Section: Drug Development Using ML For Covid-19mentioning

confidence: 99%

Battling COVID-19 using machine learning: A review

Chadaga

Prabhu

et al. 2021

Cogent Engineering

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Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2) known as Coronavirus surfaced in late 2019. It turned out to be a life-threatening disease and is causing chaos all around the world. The World Health Organisation (WHO) declared it a pandemic in March 2020. To handle COVID-19 related problems, research in many areas of science was introduced. Machine learning (ML), being one of the most successful stories in recent times is widely used to solve a variety of problems in our everyday life. Here, an overview of machine learning that tackles the pandemic is discussed in the beginning. Various datasets related to COVID-19 are also explored. Diagnosis of this viral disease using CT-Scans, X-ray images, sound analysis and blood tests using machine learning are presented in-depth. Drug and vaccine development using machine learning for COVID-19 are also discussed. Pandemic management and control were also examined. The main objective of this paper is to conduct a systematic review of machine learning applications that fight the deadly virus. This paper helps the researchers to understand and analyse the data trends related to COVID-19 and also

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“…This model focuses on finding genes most relevant to the drug sensitivity prediction rather than the complete set of genes. DeepPurpose is another model that predicts IC 50 values [61]. Another critical property considered in drug designing is the compound's aqueous solubility.…”

Section: Prediction Of Drug-target Interactionsmentioning

confidence: 99%

Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics

Vaz

Balaji

2021

Mol Divers

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Convolutional neural networks (CNNs) have been used to extract information from various datasets of different dimensions. This approach has led to accurate interpretations in several subfields of biological research, like pharmacogenomics, addressing issues previously faced by other computational methods. With the rising attention for personalized and precision medicine, scientists and clinicians have now turned to artificial intelligence systems to provide them with solutions for therapeutics development. CNNs have already provided valuable insights into biological data transformation. Due to the rise of interest in precision and personalized medicine, in this review, we have provided a brief overview of the possibilities of implementing CNNs as an effective tool for analyzing one-dimensional biological data, such as nucleotide and protein sequences, as well as small molecular data, e.g., simplified molecular-input line-entry specification, InChI, binary fingerprints, etc., to categorize the models based on their objective and also highlight various challenges. The review is organized into specific research domains that participate in pharmacogenomics for a more comprehensive understanding. Furthermore, the future intentions of deep learning are outlined.

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DeepPurpose: a deep learning library for drug–target interaction prediction

Cited by 257 publications

References 15 publications

Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2

Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2

Battling COVID-19 using machine learning: A review

Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics

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