US 2026/0301858 A1Published application

Artificial intelligence-based system for generating pocket- conditioned molecules and method thereof

AssigneeCentella Scientific Private Limited

InventorsRIYAZ SYED, POORNA CHANDRA RAO YEDLA

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Abstract

The present invention discloses an artificial intelligence-based (AI-based) system for generating pocket-conditioned molecules and an artificial intelligence-based (AI-based) method thereof. The AI-based system obtains ligand data and protein pocket data for generating molecular interactions. The AI-based system aligns two-dimensional (2D) molecular representations with three-dimensional (3D) molecular conformations and two-dimensional (2D) sequences with three-dimensional (3D) coordinates to ensure a spatial consistency and a structural consistency. The AI-based system encodes the ligand data and the protein pocket data to generate topological features and spatial features. The AI-based system determines interaction between molecular structures based on incorporating positional embeddings to the encoded ligand data and protein pocket data. The AI-based system generates the pocket-conditioned molecules based on integrating the topological features and the spatial features of the encoded ligand data and protein pocket data.

Claims12

Dependent claims

An artificial intelligence-based (AI-based) method for generating one or more pocket-conditioned molecules, comprising:

obtaining, by one or more hardware processors through a data obtaining subsystem, at least one of:
ligand data comprises at least one of:
two-dimensional (2D) molecular representations and three-dimensional (3D) molecular conformations and protein pocket data comprises at least one of:
two-dimensional (2D) sequences and three-dimensional (3D) coordinates to generate molecular interactions;
aligning, by the one or more hardware processors through a data pre-processing subsystem, at least one of:
the two-dimensional (2D) molecular representations with the three-dimensional (3D) molecular conformations and the two-dimensional (2D) sequences with the three-dimensional (3D) coordinates to verify at least one of:
a spatial consistency and a structural consistency;
encoding, by the one or more hardware processors through a data encoding subsystem configured with one or more artificial intelligence (AI) models, at least one of:
the ligand data and the protein pocket data to generate at least one of:
one or more topological features and one or more spatial features;
determining, by the one or more hardware processors through a positional embedding subsystem, interaction between one or more molecular structures based on incorporating one or more positional embeddings to the encoded at least one of:
ligand data and protein pocket data;
and
generating, by the one or more hardware processors through a molecular generation subsystem, the one or more pocket-conditioned molecules based on integrating at least one of:
the one or more topological features and the one or more spatial features of the encoded at least one of:
ligand data and protein pocket data.

13 more elements

An artificial intelligence-based (AI-based) system for generating one or more pocket-conditioned molecules, comprising:

one or more servers, comprising:
one or more hardware processors;
and
a memory unit coupled to the one or more hardware processors, wherein the memory unit comprises a plurality of subsystems in form of one or more instructions executable by the one or more hardware processors, and wherein the plurality of subsystems comprises:
a data obtaining subsystem configured to obtain at least one of:
ligand data comprises at least one of:
two-dimensional (2D) molecular representations and three-dimensional (3D) molecular conformations and protein pocket data comprises at least one of:
two-dimensional (2D) sequences and three-dimensional (3D) coordinates, for generating molecular interactions;
a data pre-processing subsystem configured to align at least one of:
the two-dimensional (2D) molecular representations with the three-dimensional (3D) molecular conformations and the two-dimensional (2D) sequences with the three-dimensional (3D) coordinates to verify at least one of:
a spatial consistency and a structural consistency;
a data encoding subsystem configured with one or more artificial intelligence (AI) models to encode at least one of:
the ligand data and the protein pocket data to generate at least one of:
one or more topological features and one or more spatial features;
a positional embedding subsystem configured to determine interaction between one or more molecular structures based on incorporating one or more positional embeddings to the encoded at least one of:
ligand data and protein pocket data;
and
a molecular generation subsystem configured to generate the one or more pocket-conditioned molecules based on integrating at least one of:
the one or more topological features and the one or more spatial features of the encoded at least one of:
ligand data and protein pocket data.

17 more elements

Dependencies are inferred from the claim text.

Facts

Application: 19/231,626Filed 9 Jun 2025
Priority: IN 20254102843826 Mar 2025
Published: 1 Oct 2026
Term ends: Not estimatedThe source records don’t support an estimate.

Classification7 codes

G16B 15/30
ICT specially adapted for analysing two-dimensional [2D] or three-dimensional [3D] molecular structures, e.g. structural or functional relations…:…
G06N 3/042
Architecture, e.g. interconnection topology: knowledge-based neural networks; Logical representations of neural networks
G06N 3/0475
Architecture, e.g. interconnection topology: generative networks
G16B 15/20
ICT specially adapted for analysing two-dimensional [2D] or three-dimensional [3D] molecular structures, e.g. structural or functional relations…:…