Patents by Inventor Mark Veselov

Mark Veselov has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Subset conditioning using variational autoencoder with a learnable tensor train induced prior
    Patent number: 11893498
    Abstract: The proposed model is a Variational Autoencoder having a learnable prior that is parametrized with a Tensor Train (VAE-TTLP). The VAE-TTLP can be used to generate new objects, such as molecules, that have specific properties and that can have specific biological activity (when a molecule). The VAE-TTLP can be trained in a way with the Tensor Train so that the provided data may omit one or more properties of the object, and still result in an object with a desired property.
    Type: Grant
    Filed: February 27, 2023
    Date of Patent: February 6, 2024
    Assignee: INSILICO MEDICINE IP LIMITED
    Inventors: Aleksandr Aliper, Aleksandrs Zavoronkovs, Alexander Zhebrak, Daniil Polykovskiy, Maksim Kuznetsov, Yan Ivanenkov, Mark Veselov, Vladimir Aladinskiy, Evgeny Putin, Yuriy Volkov, Arip Asadulaev
  • SUBSET CONDITIONING USING VARIATIONAL AUTOENCODER WITH A LEARNABLE TENSOR TRAIN INDUCED PRIOR
    Publication number: 20230214662
    Abstract: The proposed model is a Variational Autoencoder having a learnable prior that is parametrized with a Tensor Train (VAE-TTLP). The VAE-TTLP can be used to generate new objects, such as molecules, that have specific properties and that can have specific biological activity (when a molecule). The VAE-TTLP can be trained in a way with the Tensor Train so that the provided data may omit one or more properties of the object, and still result in an object with a desired property.
    Type: Application
    Filed: February 27, 2023
    Publication date: July 6, 2023
    Inventors: Aleksandr Aliper, Aleksandrs Zavoronkovs, Alexander Zhebrak, Daniil Polykovskiy, Maksim Kuznetsov, Yan Ivanenkov, Mark Veselov, Vladimir Aladinskiy, Evgeny Putin, Yuriy Volkov, Arip Asadulaev
  • Subset conditioning using variational autoencoder with a learnable tensor train induced prior
    Patent number: 11593660
    Abstract: The proposed model is a Variational Autoencoder having a learnable prior that is parametrized with a Tensor Train (VAE-TTLP). The VAE-TTLP can be used to generate new objects, such as molecules, that have specific properties and that can have specific biological activity (when a molecule). The VAE-TTLP can be trained in a way with the Tensor Train so that the provided data may omit one or more properties of the object, and still result in an object with a desired property.
    Type: Grant
    Filed: September 18, 2018
    Date of Patent: February 28, 2023
    Assignee: INSILICO MEDICINE IP LIMITED
    Inventors: Aleksandr Aliper, Aleksandrs Zavoronkovs, Alexander Zhebrak, Daniil Polykovskiy, Maksim Kuznetsov, Yan Ivanenkov, Mark Veselov, Vladimir Aladinskiy, Evgeny Putin, Yuriy Volkov, Arip Asadulaev
  • SUBSET CONDITIONING USING VARIATIONAL AUTOENCODER WITH A LEARNABLE TENSOR TRAIN INDUCED PRIOR
    Publication number: 20200090049
    Abstract: The proposed model is a Variational Autoencoder having a learnable prior that is parametrized with a Tensor Train (VAE-TTLP). The VAE-TTLP can be used to generate new objects, such as molecules, that have specific properties and that can have specific biological activity (when a molecule). The VAE-TTLP can be trained in a way with the Tensor Train so that the provided data may omit one or more properties of the object, and still result in an object with a desired property.
    Type: Application
    Filed: September 18, 2018
    Publication date: March 19, 2020
    Inventors: Aleksandr Aliper, Aleksandrs Zavoronkovs, Alexander Zhebrak, Daniil Polykovskiy, Maksim Kuznetsov, Yan Ivanenkov, Mark Veselov, Vladimir Aladinskiy, Evgeny Putin, Yuriy Volkov, Arip Asadulaev