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generative tensorial reinforcement learning (gentrl)  (Insilico Medicine)

 
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    Insilico Medicine generative tensorial reinforcement learning (gentrl)
    Generative Tensorial Reinforcement Learning (Gentrl), supplied by Insilico Medicine, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/generative+tensorial+reinforcement+learning+(gentrl)/generative+tensorial+reinforcement+learning++gentrl++model/pm37331692-162-7-2
    Average 90 stars, based on 1 article reviews
    generative tensorial reinforcement learning (gentrl) - by Bioz Stars, 2026-10
    90/100 stars

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    Related Articles

    Plasmid Preparation:

    Article Title: A Novel Scalarized Scaffold Hopping Algorithm with Graph-Based Variational Autoencoder for Discovery of JAK1 Inhibitors
    Article Snippet: Utilizing deep learning in drug discovery, especially in generative chemistry, has brought big progress and attention since Insilico Medicine reported their generative tensorial reinforcement learning (GENTRL) algorithm for identification of novel DDR1 inhibitors in weeks.

    Article Title: Natural product drug discovery in the artificial intelligence era
    Article Snippet: In 2019, the American company Insilico Medicine developed an AI system named GENTRL (for Generative Tensorial Reinforcement Learning) that successfully invented six kinase inhibitors of discoidin domain receptor 1 linked to lung fibrosis, in just 46 days.

    Article Title: AI in drug discovery and its clinical relevance
    Article Snippet: Insilico Medicine developed Generative Tensorial Reinforcement Learning (GENTRL) AI, a system that can discover and successfully test new compounds in 46 days, making the whole process 15 times faster .

    Article Title: Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities
    Article Snippet: In another scientific breakthrough, to combat fibrosis, Hongkong/New York-based Insilico Medicine developed a GAI model GENTRL (Generative Tensorial Reinforcement Learning) to identify novel kinase DDR1 inhibitors.

    Article Title: Designing Novel Compound Candidates Against SARS-CoV-2 Using Generative Deep Neural Networks and Cheminformatics
    Article Snippet: In September 2019, Insilico Medicine released a deep generative model (Generative tensorial reinforcement learning, GENTRL), a deep learning method for performing de novo drug design [ ].

    Article Title: Iterative machine learning-based chemical similarity search to identify novel chemical inhibitors.
    Article Snippet: A generative tensorial reinforcement learning (GENTRL) model developed by Insilico Medicine led to the discovery of novel potent DDR1 kinase inhibitors in a short time period [2].

    Article Title: Iterative machine learning-based chemical similarity search to identify novel chemical inhibitors
    Article Snippet: A generative tensorial reinforcement learning (GENTRL) model developed by Insilico Medicine led to the discovery of novel potent DDR1 kinase inhibitors in a short time period [ ].

    Modification:

    Article Title: A Novel Scalarized Scaffold Hopping Algorithm with Graph-Based Variational Autoencoder for Discovery of JAK1 Inhibitors
    Article Snippet: Utilizing deep learning in drug discovery, especially in generative chemistry, has brought big progress and attention since Insilico Medicine reported their generative tensorial reinforcement learning (GENTRL) algorithm for identification of novel DDR1 inhibitors in weeks.

    Article Title: Natural product drug discovery in the artificial intelligence era
    Article Snippet: In 2019, the American company Insilico Medicine developed an AI system named GENTRL (for Generative Tensorial Reinforcement Learning) that successfully invented six kinase inhibitors of discoidin domain receptor 1 linked to lung fibrosis, in just 46 days.

    Article Title: AI in drug discovery and its clinical relevance
    Article Snippet: Insilico Medicine developed Generative Tensorial Reinforcement Learning (GENTRL) AI, a system that can discover and successfully test new compounds in 46 days, making the whole process 15 times faster .

    Article Title: Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities
    Article Snippet: In another scientific breakthrough, to combat fibrosis, Hongkong/New York-based Insilico Medicine developed a GAI model GENTRL (Generative Tensorial Reinforcement Learning) to identify novel kinase DDR1 inhibitors.

    Article Title: Designing Novel Compound Candidates Against SARS-CoV-2 Using Generative Deep Neural Networks and Cheminformatics
    Article Snippet: In September 2019, Insilico Medicine released a deep generative model (Generative tensorial reinforcement learning, GENTRL), a deep learning method for performing de novo drug design [ ].

    Article Title: Iterative machine learning-based chemical similarity search to identify novel chemical inhibitors.
    Article Snippet: A generative tensorial reinforcement learning (GENTRL) model developed by Insilico Medicine led to the discovery of novel potent DDR1 kinase inhibitors in a short time period [2].

    Article Title: Iterative machine learning-based chemical similarity search to identify novel chemical inhibitors
    Article Snippet: A generative tensorial reinforcement learning (GENTRL) model developed by Insilico Medicine led to the discovery of novel potent DDR1 kinase inhibitors in a short time period [ ].

    Drug discovery:

    Article Title: A Novel Scalarized Scaffold Hopping Algorithm with Graph-Based Variational Autoencoder for Discovery of JAK1 Inhibitors
    Article Snippet: Utilizing deep learning in drug discovery, especially in generative chemistry, has brought big progress and attention since Insilico Medicine reported their generative tensorial reinforcement learning (GENTRL) algorithm for identification of novel DDR1 inhibitors in weeks.

    Article Title: Natural product drug discovery in the artificial intelligence era
    Article Snippet: In 2019, the American company Insilico Medicine developed an AI system named GENTRL (for Generative Tensorial Reinforcement Learning) that successfully invented six kinase inhibitors of discoidin domain receptor 1 linked to lung fibrosis, in just 46 days.

    Article Title: AI in drug discovery and its clinical relevance
    Article Snippet: Insilico Medicine developed Generative Tensorial Reinforcement Learning (GENTRL) AI, a system that can discover and successfully test new compounds in 46 days, making the whole process 15 times faster .

    Article Title: Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities
    Article Snippet: In another scientific breakthrough, to combat fibrosis, Hongkong/New York-based Insilico Medicine developed a GAI model GENTRL (Generative Tensorial Reinforcement Learning) to identify novel kinase DDR1 inhibitors.

    Article Title: Designing Novel Compound Candidates Against SARS-CoV-2 Using Generative Deep Neural Networks and Cheminformatics
    Article Snippet: In September 2019, Insilico Medicine released a deep generative model (Generative tensorial reinforcement learning, GENTRL), a deep learning method for performing de novo drug design [ ].

    Article Title: Iterative machine learning-based chemical similarity search to identify novel chemical inhibitors.
    Article Snippet: A generative tensorial reinforcement learning (GENTRL) model developed by Insilico Medicine led to the discovery of novel potent DDR1 kinase inhibitors in a short time period [2].

    Article Title: Iterative machine learning-based chemical similarity search to identify novel chemical inhibitors
    Article Snippet: A generative tensorial reinforcement learning (GENTRL) model developed by Insilico Medicine led to the discovery of novel potent DDR1 kinase inhibitors in a short time period [ ].

    Binding Assay:

    Article Title: A Novel Scalarized Scaffold Hopping Algorithm with Graph-Based Variational Autoencoder for Discovery of JAK1 Inhibitors
    Article Snippet: Utilizing deep learning in drug discovery, especially in generative chemistry, has brought big progress and attention since Insilico Medicine reported their generative tensorial reinforcement learning (GENTRL) algorithm for identification of novel DDR1 inhibitors in weeks.

    Article Title: Natural product drug discovery in the artificial intelligence era
    Article Snippet: In 2019, the American company Insilico Medicine developed an AI system named GENTRL (for Generative Tensorial Reinforcement Learning) that successfully invented six kinase inhibitors of discoidin domain receptor 1 linked to lung fibrosis, in just 46 days.

    Article Title: AI in drug discovery and its clinical relevance
    Article Snippet: Insilico Medicine developed Generative Tensorial Reinforcement Learning (GENTRL) AI, a system that can discover and successfully test new compounds in 46 days, making the whole process 15 times faster .

    Article Title: Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities
    Article Snippet: In another scientific breakthrough, to combat fibrosis, Hongkong/New York-based Insilico Medicine developed a GAI model GENTRL (Generative Tensorial Reinforcement Learning) to identify novel kinase DDR1 inhibitors.

    Article Title: Designing Novel Compound Candidates Against SARS-CoV-2 Using Generative Deep Neural Networks and Cheminformatics
    Article Snippet: In September 2019, Insilico Medicine released a deep generative model (Generative tensorial reinforcement learning, GENTRL), a deep learning method for performing de novo drug design [ ].

    Article Title: Iterative machine learning-based chemical similarity search to identify novel chemical inhibitors.
    Article Snippet: A generative tensorial reinforcement learning (GENTRL) model developed by Insilico Medicine led to the discovery of novel potent DDR1 kinase inhibitors in a short time period [2].

    Article Title: Iterative machine learning-based chemical similarity search to identify novel chemical inhibitors
    Article Snippet: A generative tensorial reinforcement learning (GENTRL) model developed by Insilico Medicine led to the discovery of novel potent DDR1 kinase inhibitors in a short time period [ ].



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    Applications of AI-based methods at different stages of a drug discovery pipeline. There are about 2700 known potential drug target proteins in the human body and about 9600 FDA-approved small molecule drugs , , . Machine learning can be used to identify the targeted protein, GNNs can be used for predicting drug-target interactions and binding affinity, and <t>reinforcement</t> learning can be used to optimize the properties of a molecule. Computer vision can determine the spatial state of the tumor microenvironment. <t>Generative</t> models can be employed to design new molecules, simulation-based studies can suggest properties of protein-drug complexes, such as stability and dynamics, and NLP can be used to mine the existing scientific literature for drug re-purposing, FDA review, and post-market analysis.
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    Image Search Results


    Applications of AI-based methods at different stages of a drug discovery pipeline. There are about 2700 known potential drug target proteins in the human body and about 9600 FDA-approved small molecule drugs , , . Machine learning can be used to identify the targeted protein, GNNs can be used for predicting drug-target interactions and binding affinity, and reinforcement learning can be used to optimize the properties of a molecule. Computer vision can determine the spatial state of the tumor microenvironment. Generative models can be employed to design new molecules, simulation-based studies can suggest properties of protein-drug complexes, such as stability and dynamics, and NLP can be used to mine the existing scientific literature for drug re-purposing, FDA review, and post-market analysis.

    Journal: Heliyon

    Article Title: AI in drug discovery and its clinical relevance

    doi: 10.1016/j.heliyon.2023.e17575

    Figure Lengend Snippet: Applications of AI-based methods at different stages of a drug discovery pipeline. There are about 2700 known potential drug target proteins in the human body and about 9600 FDA-approved small molecule drugs , , . Machine learning can be used to identify the targeted protein, GNNs can be used for predicting drug-target interactions and binding affinity, and reinforcement learning can be used to optimize the properties of a molecule. Computer vision can determine the spatial state of the tumor microenvironment. Generative models can be employed to design new molecules, simulation-based studies can suggest properties of protein-drug complexes, such as stability and dynamics, and NLP can be used to mine the existing scientific literature for drug re-purposing, FDA review, and post-market analysis.

    Article Snippet: Insilico Medicine developed Generative Tensorial Reinforcement Learning (GENTRL) AI, a system that can discover and successfully test new compounds in 46 days, making the whole process 15 times faster .

    Techniques: Drug discovery, Binding Assay