Title:
特定の生物学的標的に対して生物学的活性を有する化合物を生成するためのワークフロー
Document Type and Number:
Japanese Patent JP7382489
Kind Code:
B2
Abstract:
A computer-implemented method can include: receiving input of a biological target; receiving a generative model (e.g., tensorial reinforcement learning (GENTRL) model or other model) trained with reference compounds, wherein the reference compounds include: general compounds, compounds that modulate the biological target, and compounds that modulate biomolecules other than the biological target; generating structures of generated compounds with the generative model; prioritizing structures of generated compounds based on at least one criteria; processing prioritized chemical structures of the generated compounds through a Sammon mapping protocol to obtain hit structures; and providing chemical structures of the hit structures. One or more non-transitory computer readable media are provided that store instructions that in response to being executed by one or more processors, cause a computer system to perform operations, the operations comprising performing the computer methods described herein for providing chemical structure of hit structures generated by the generative model.
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Inventors:
Zaboronkofs, Alexander
Ivanenkov, Jan
Polikowski, Daniil
Alipel, Alexandre
Ivanenkov, Jan
Polikowski, Daniil
Alipel, Alexandre
Application Number:
JP2022511189A
Publication Date:
November 16, 2023
Filing Date:
August 22, 2020
Export Citation:
Assignee:
In silico medicine IP Limited
International Classes:
G16C20/50
Domestic Patent References:
| JP2011509071A |
Foreign References:
| WO2019100158A1 | ||||
| WO2007139037A1 | ||||
| CN109988151A | ||||
| WO2019018780A1 |
Other References:
Daniil Polykovskiy,Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery,molecular pharmaceutics,ACS Publications,2018年,p.4398-4405
Attorney, Agent or Firm:
Sonoda & Kobayashi Patent Attorneys Corporation