Invention Grant
- Patent Title: Generative machine learning systems for drug design
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Application No.: US15015044Application Date: 2016-02-03
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Publication No.: US10776712B2Publication Date: 2020-09-15
- Inventor: Kenta Oono , Justin Clayton , Nobuyuki Ota
- Applicant: PREFERRED NETWORKS, INC.
- Applicant Address: JP Tokyo
- Assignee: Preferred Networks, Inc.
- Current Assignee: Preferred Networks, Inc.
- Current Assignee Address: JP Tokyo
- Agency: IPUSA, PLLC
- Main IPC: G06N20/00
- IPC: G06N20/00 ; G06N3/04 ; G16C20/50 ; G06N7/00 ; G16C20/70

Abstract:
In various embodiments, the systems and methods described herein relate to generative models. The generative models may be trained using machine learning approaches, with training sets comprising chemical compounds and biological or chemical information that relate to the chemical compounds. Deep learning architectures may be used. In various embodiments, the generative models are used to generate chemical compounds that have desired characteristics, e.g. activity against a selected target. The generative models may be used to generate chemical compounds that satisfy multiple requirements.
Public/Granted literature
- US20170161635A1 GENERATIVE MACHINE LEARNING SYSTEMS FOR DRUG DESIGN Public/Granted day:2017-06-08
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