公开(公告)号：US20200152288A1

公开(公告)日：2020-05-14

申请号：US16504184

申请日：2019-07-05

Applicant: Tata Consultancy Services Limited

Inventor： Rajgopal SRINIVASAN ,  Akriti JAIN ,  Poulami CHAUDHURI

IPC: G16B20/20 ,  G16B30/10

Abstract: This disclosure relates generally to method and system for predicting effect of genomic variations on pre-mRNA splicing. The method include receiving genomic position information of at least one candidate variant, gene transcripts and genomic coordinates information of the gene transcripts; classifying the at least one candidate variant into one of a splice acceptor site region and a branch site region based on the coordinates information of the gene transcripts and the genomic position information of at least one candidate variant; evaluating effect of the at least one candidate variant on pre-mRNA splicing, based on a classified region from the classification of the at least one candidate variant and predicting pathogenicity of the at least one candidate variant based on the evaluated effect of the at least one candidate variant on the pre-mRNA splicing.

公开(公告)号：US20220157401A1

公开(公告)日：2022-05-19

申请号：US17593240

申请日：2020-03-13

Applicant: Tata Consultancy Services Limited

Inventor： Kavya Naga Sai VADDADI ,  Naveen SIVADASAN ,  Rajgopal SRINIVASAN

IPC: G16B30/10 ,  G16B40/30

Abstract: There is a demand for low-cost efficient robust method for mapping read sequences with genome variation graph in genomic study. This disclosure herein relates to a method and system for mapping read sequences with genome variation graph by constructing a subgraph using a novel combination of graph embedding and graph winnowing techniques. The system processes the obtained plurality of read sequences and a genome variation graph for constructing the subgraph by computing an embedding for the genome variation graph utilizing a graph embedding technique. Further, graph index is generated for the genome variation graph based on the embedding and the genome variation graph utilizing the graph winnowing technique. Then computes gapped alignment score for read sequence (r) with its corresponding subgraph. Thus, enables a reliable method for read sequence with accurate, memory efficient and scalable system for mapping read sequences with genome variation graph.

公开(公告)号：US20240170108A1

公开(公告)日：2024-05-23

申请号：US18490025

申请日：2023-10-19

Applicant: Tata Consultancy Services Limited

Inventor： Broto CHAKRABARTY ,  Siladitya PADHI ,  Riya Dilipbhai SADRANI ,  Rajgopal SRINIVASAN ,  Arijit ROY

IPC: G16C20/50 ,  G16B20/00 ,  G16B40/20 ,  G16C20/70 ,  G16H70/40

CPC classification number: G16C20/50 ,  G16B20/00 ,  G16B40/20 ,  G16C20/70 ,  G16H70/40

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. Moreover, the embodiments herein further provide a mechanism of screening of drug-like molecules using the machine learning model(s).

公开(公告)号：US20230154573A1

公开(公告)日：2023-05-18

申请号：US17969021

申请日：2022-10-19

Applicant: Tata Consultancy Services Limited

Inventor： Arijit ROY ,  Rajgopal SRINIVASAN ,  Sarveswara Rao VANGALA ,  Sowmya Ramaswamy KRISHNAN ,  Navneet BUNG ,  Gopalakrishnan BULUSU

IPC: G16C20/50 ,  G16C20/70

CPC classification number: G16C20/50 ,  G16C20/70

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.