The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Aug. 12, 2025

Filed:

Oct. 11, 2021
Applicant:

Tata Consultancy Services Limited, Mumbai, IN;

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 3/04 (2023.01); G06F 9/50 (2006.01); G06F 9/54 (2006.01); G06F 18/214 (2023.01); G06N 3/042 (2023.01); G06N 3/08 (2023.01); G16C 20/30 (2019.01); G16C 20/70 (2019.01);
U.S. Cl.
CPC ...
G06N 3/04 (2013.01); G06F 9/5044 (2013.01); G06F 9/546 (2013.01); G06F 18/2155 (2023.01); G06N 3/042 (2023.01); G06N 3/08 (2013.01); G16C 20/30 (2019.02); G16C 20/70 (2019.02);
Abstract

This disclosure relates generally to system and method for molecular property prediction using hypergraph message passing neural network (HMPNN). Typical MPNN architectures used for chemical graphs representation learning have limitations, including, inefficiency to learn long-range dependencies for homogeneous graphs, ineffectiveness to model topological properties of graphs taking into consideration the multiscale representations, and lack of anti-smoothing weighting mechanism to address graphs random walk limit distribution. Disclosed method and system HyperGraph attention-driven Hypergraph Convolution. The Hypergraph attention driven convolution, on molecular hypergraph results in learning efficient embeddings on the high-order molecular graph-structured data. By taking into account the transient incidence matrix, the induced inductive bias augments the scope of molecular hypergraph representation learning.


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