Invention Application
- Patent Title: TRANSFER LEARNING FOR MOLECULAR STRUCTURE GENERATION
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Application No.: US16886160Application Date: 2020-05-28
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Publication No.: US20210374551A1Publication Date: 2021-12-02
- Inventor: Enara C Vijil , Payel Das , Inkit Padhi
- Applicant: International Business Machines Corporation
- Applicant Address: US NY Armonk
- Assignee: International Business Machines Corporation
- Current Assignee: International Business Machines Corporation
- Current Assignee Address: US NY Armonk
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06N20/00 ; G06K9/62

Abstract:
Techniques regarding generating molecular structures with attributes of interest are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a transfer learning component that determines a molecular structure of a compound by employing a transfer learning process that utilizes lessons learned from an unconditional generative machine learning model to train a conditional machine learning model that regards a target attribute profile.
Public/Granted literature
- US12079730B2 Transfer learning for molecular structure generation Public/Granted day:2024-09-03
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