Patents by Inventor Arijit ROY

Arijit ROY 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).

  • Publication number: 20250063528
    Abstract: According to an example embodiment of the disclosure, a method for performing network slice reselection by a User Equipment (UE) is disclosed. The method comprises identifying allowed network slices within a first Network Slice Simultaneous Registration Group (NSSRG) for first applications at the UE. The method comprises receiving an initialization request from a second application at the UE. The method comprises determining one or more UE Route Selection Policy (URSP) rules for the second application. The method comprises determining whether the network slice required for the second application corresponds to one of the one or more allowed network slices within the first NSSRG. The method comprises transmitting, to a network, a second registration request for registration of the UE to a second NSSRG including the network slice required for the second application based on the URSP rules for the second application.
    Type: Application
    Filed: June 25, 2024
    Publication date: February 20, 2025
    Inventors: Jagadeesh GANDIKOTA, Koustav ROY, Danish Ehsan HASHMI, Arijit SEN, Jyotirmoy KARJEE, Lalith KUMAR
  • Publication number: 20250037805
    Abstract: Deep learning-based generative models have improved the exploration of chemical space in small molecule drug discovery. Although thousands of novel small molecules can be generated with such models, synthesizing them still remains a challenging task. In literature, several methods have been proposed to predict the synthetic route of a target molecule by working backwards to find the most suitable starting reactants (retrosynthesis). While retrosynthesis is shown to be successful, for novel molecules it is often difficult to find the synthesis path. System and method of the present disclosure generate molecules along with its synthesis route and also provide an insight into the interactions in the active site of target protein, using graph convolution networks (GCNs) and Monte Carlo tree search (MCTS). A target-specific bioactivity prediction model is used as the scoring function to navigate the MCTS search space efficiently.
    Type: Application
    Filed: June 24, 2024
    Publication date: January 30, 2025
    Applicant: Tata Consultancy Services Limited
    Inventors: SOWMYA RAMASWAMY KRISHNAN, ARIJIT ROY, NAVNEET BUNG, RAJGOPAL SRINIVASAN
  • Patent number: 12190999
    Abstract: Conventionally, deep learning-based methods have shown some success in ligand-based drug design. However, these methods face data scarcity problems while designing drugs against novel targets. Embodiments of the present disclosure provide systems and methods that leverage the potential of deep learning and molecular modeling approaches to develop a drug design pipeline, which can be useful for cases where there is limited or no availability of target-specific ligand datasets. Inhibitors of other proteins, structurally similar to the target protein are screened at the active site of the target protein to create an initial target-specific dataset. Transfer learning is implemented to learn features of target-specific dataset and design new chemical entities/molecules using a deep generative model. A deep predictive model is used predict docking scores of newly designed/identified molecules.
    Type: Grant
    Filed: December 29, 2020
    Date of Patent: January 7, 2025
    Assignee: TATA CONSULTANCY SERVICES LIMITED
    Inventors: Arijit Roy, Sowmya Ramaswamy Krishnan, Navneet Bung, Gopalakrishnan Bulusu
  • Publication number: 20240331808
    Abstract: The embodiments of present disclosure herein address the inability of existing techniques to fragment both small molecules and substituents of a core scaffold. It addresses generation of lesser number of unique fragments which hinders application of graph propagation approaches to predict properties from molecular datasets. The method and system for extraction of small molecule fragments and their explanation for drug-like properties. A molecular graph representation is used to train graph convolution network (GCN) models for prediction of various absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. The models developed are compared with an existing atom-level graph model trained using a similar architecture. Further, the explanations obtained from the predictive models are validated based on their relevance to the existing knowledgebase of substructure contributions using matched molecular pairs (MMP) analysis.
    Type: Application
    Filed: January 31, 2024
    Publication date: October 3, 2024
    Applicant: Tata Consultancy Services Limited
    Inventors: NAVNEET BUNG, RAJGOPAL SRINIVASAN, SARVESWARA RAO VANGALA, SOWMYA RAMASWAMY KRISHNAN, ARIJIT ROY
  • Publication number: 20240257908
    Abstract: Drug induced gene expression provides information covering various aspects of drug discovery and development. Recent advances in accessibility of open-source drug-induced transcriptomic data along with ability of deep learning algorithms to understand hidden patterns have opened opportunity for designing drug molecules based on desired gene expression signatures. Embodiments herein provide method and system for cell specific model where gene expressions are processed via pretrained Simplified Molecular Input Line Entry System (SMILES) variational autoencoder (s-VAE) to produce new molecules. The model is trained with drug and drug induced gene expression data as input. Both pretrained s-VAE and profile variational autoencoder (p-VAE) are trained jointly. During joint training, difference between newly generated molecules and existing drug molecules is calculated as joint loss function composed of binary cross entropy loss and Kullback-Leibler divergence loss.
    Type: Application
    Filed: October 31, 2023
    Publication date: August 1, 2024
    Applicant: Tata Consultancy Services Limited
    Inventors: Dibyajyoti Das, Arijit Roy, Rajgopal Srinivasan, Broto Chakrabarty
  • Publication number: 20240170108
    Abstract: Traditional drug discovery methods are target-based, time- and resource-intensive, and require a lot of resources for the initial hit molecule identification. Phenotype-based drug screening requires differential gene expression data of a large number of molecules for different combinations of cell-line, time point and dosage. Experimentally obtaining gene expression data for all these combinations is again a heavily resource-intensive process. The technical challenge in conventional methods that use prediction models is that they depend largely on the data processing and representation. The disclosure herein generally relates to drug-like molecule screening, and, more particularly, to a method and system for gene expression and machine learning-based drug screening. The embodiment, thus, provides a mechanism of a small molecule-induced gene expression prediction based on machine learning models.
    Type: Application
    Filed: October 19, 2023
    Publication date: May 23, 2024
    Applicant: Tata Consultancy Services Limited
    Inventors: Broto CHAKRABARTY, Siladitya PADHI, Riya Dilipbhai SADRANI, Rajgopal SRINIVASAN, Arijit ROY
  • Patent number: 11978537
    Abstract: Pathogens invade and infect humans. Understanding the infection mechanism is essential for determining targets for new therapeutics. Existing methods provide too many false positive results. A method and system for predicting protein-protein interaction between a host and a pathogen has been provided. The disclosure provides a pipeline for predicting HPIs, which is a combination of biological knowledge-based filters, domain-based filter and sequence-based predictions. Biologically feasible interactions are only possible when both the proteins share common localization and overlapping expression profiles. This observation was used as the first filter to remove biologically irrelevant HPIs. Proteins interact with each other through domains. Both interacting and non-interacting protein pairs provide valuable information about the probability of protein-protein interactions and hence both were used to derive statistical inferences to remove improbable HPIs.
    Type: Grant
    Filed: November 17, 2020
    Date of Patent: May 7, 2024
    Assignee: Tata Consultancy Services Limited
    Inventors: Arijit Roy, Dibyajyoti Das, Gopalakrishnan Bulusu
  • Publication number: 20230154573
    Abstract: This disclosure relates generally to method and system for structure-based drug design using a multi-modal deep learning model. The method processes a target protein for designing at least one optimized molecule by using a multi-modal deep learning model. The GAT-VAE module obtains a latent vector of at least one active site graph comprising of key amino acid residues from the target protein. The SMILES-VAE module obtains at least one latent vector from the target protein. Further, the conditional molecular generator concatenates the active site graph with the latent vector to generate a set of molecules. The RL framework is iteratively performed on the concatenated latent vector to optimize at least one molecule by using the drug-target affinity (DTA) predictor module to predict an affinity value for the set of molecules towards the target protein. Further, at least one optimized molecule is designed with an affinity of the target protein.
    Type: Application
    Filed: October 19, 2022
    Publication date: May 18, 2023
    Applicant: Tata Consultancy Services Limited
    Inventors: Arijit ROY, Rajgopal SRINIVASAN, Sarveswara Rao VANGALA, Sowmya Ramaswamy KRISHNAN, Navneet BUNG, Gopalakrishnan BULUSU
  • Publication number: 20220084627
    Abstract: Conventionally, deep learning-based methods have shown some success in ligand-based drug design. However, these methods face data scarcity problems while designing drugs against novel targets. Embodiments of the present disclosure provide systems and methods that leverage the potential of deep learning and molecular modeling approaches to develop a drug design pipeline, which can be useful for cases where there is limited or no availability of target-specific ligand datasets. Inhibitors of other proteins, structurally similar to the target protein are screened at the active site of the target protein to create an initial target-specific dataset. Transfer learning is implemented to learn features of target-specific dataset and design new chemical entities/molecules using a deep generative model. A deep predictive model is used predict docking scores of newly designed/identified molecules.
    Type: Application
    Filed: December 29, 2020
    Publication date: March 17, 2022
    Applicant: Tata Consultancy Services Limited
    Inventors: Arijit ROY, Sowmya RAMASWAMY KRISHNAN, Navneet BUNG, Gopalakrishnan BULUSU
  • Patent number: 11157390
    Abstract: Disclosed embodiments provide techniques for automatic software defect correction of a computer program. Computer program log files are scanned to identify runtime errors, corresponding to software defects. The software defects are analyzed to determine an error type, and identify the source file/code that caused the error. A solution template repository is searched for a solution template corresponding to the identified error type. If a solution template is found, the source code is checked out from the identified source repository. The template is applied to the “original” checked out source file to create a new source file with the fix, which is then uploaded back to the repository. A new software distribution is automatically built with the new source file, and the new software distribution is automatically deployed to the devices that experienced the error. Thus, defects can be automatically detected, repaired, and deployed without human intervention.
    Type: Grant
    Filed: June 28, 2019
    Date of Patent: October 26, 2021
    Assignee: International Business Machines Corporation
    Inventors: Ajoy Acharyya, Arijit Roy
  • Publication number: 20210151121
    Abstract: Pathogens invade and infect humans. Understanding the infection mechanism is essential for determining targets for new therapeutics. Existing methods provide too many false positive results. A method and system for predicting protein-protein interaction between a host and a pathogen has been provided. The disclosure provides a pipeline for predicting HPIs, which is a combination of biological knowledge-based filters, domain-based filter and sequence-based predictions. Biologically feasible interactions are only possible when both the proteins share common localization and overlapping expression profiles. This observation was used as the first filter to remove biologically irrelevant HPIs. Proteins interact with each other through domains. Both interacting and non-interacting protein pairs provide valuable information about the probability of protein-protein interactions and hence both were used to derive statistical inferences to remove improbable HPIs.
    Type: Application
    Filed: November 17, 2020
    Publication date: May 20, 2021
    Applicant: Tata Consultancy Services Limited
    Inventors: Arijit ROY, Dibyajyoti DAS, Gopalakrishnan BULUSU
  • Publication number: 20200409819
    Abstract: Disclosed embodiments provide techniques for automatic software defect correction of a computer program. Computer program log files are scanned to identify runtime errors, corresponding to software defects. The software defects are analyzed to determine an error type, and identify the source file/code that caused the error. A solution template repository is searched for a solution template corresponding to the identified error type. If a solution template is found, the source code is checked out from the identified source repository. The template is applied to the “original” checked out source file to create a new source file with the fix, which is then uploaded back to the repository. A new software distribution is automatically built with the new source file, and the new software distribution is automatically deployed to the devices that experienced the error. Thus, defects can be automatically detected, repaired, and deployed without human intervention.
    Type: Application
    Filed: June 28, 2019
    Publication date: December 31, 2020
    Applicant: International Business Machines Corporation
    Inventors: Ajoy Acharyya, Arijit Roy
  • Publication number: 20200215085
    Abstract: Methods for prevention and treatment of asthma attacks involve the administration of one or more TRPV1 antagonists, one or more LPAr antagonists or preferably a combination of one or more TRPV1 antagonists and one or more LPAr antagonists. TRPV1 antagonists and/or LPAr antagonists or a combination of both inhibit or prevent carotid body activation during an acute asthma attack. TRPV1 antagonists, LPAr antagonists or a combination thereof are useful for preventing or ameliorating the symptoms of asthma attacks. Pharmaceutical compositions for use in treating asthma and more specifically for preventing or treating asthma attacks comprise a combination of a TRPV1 antagonist and an LPAr antagonist. Methods for making medicaments for such treatment are provided. Also provided are kits for treating asthma and for preventing or treating asthma attacks in which a TRPV1 antagonist and an LPAr antagonist are separately formulated for administration at the same time.
    Type: Application
    Filed: July 19, 2018
    Publication date: July 9, 2020
    Inventors: Richard J. A. WILSON, Nicholas JENDZJOWSKY, Arijit ROY