# Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules

https://mdr.nims.go.jp/datasets/bc9f76d8-2481-4303-ad9f-dd1aa04d9a09

## File

- [cr5c00689.pdf](https://mdr.nims.go.jp/filesets/b01e5f39-7f11-4a5e-84e6-4a1bd65dcabd/download) ([Detail](https://mdr.nims.go.jp/filesets/b01e5f39-7f11-4a5e-84e6-4a1bd65dcabd.md))

## Id

bc9f76d8-2481-4303-ad9f-dd1aa04d9a09

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-09-15T21:04:37.191448Z

## Updated at

2026-09-16T01:38:41.107274Z

## Published at

2026-09-16T03:27:18.794771Z

## Doi



## First published url

https://doi.org/10.1021/acs.chemrev.5c00689

## Date published

2026-03-11

## Recorded date published

2026-3-11

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: 'Molecular Design with Artificial Intelligence: Progress and Perspectives
    for Small Molecules'
  title_type: original
  lang: en

## Description

- description: 'Progress in chemistry has been driven by the streamlining of inverse
    problem-solving methods. In the history of chemistry, several revolutionary technologies
    have led to leaps forward: the establishment of atomistic theory in the 19th century,
    structural analysis by spectroscopy in the 20th century, and the development of
    simulation by theoretical chemistry. Currently, chemistry is about to make a significant
    leap forward by integrating generative artificial intelligence (AI). In 2016,
    deep learning techniques were introduced in this domain, leading to explosive
    development. This paper reviews the development path, including traditional models
    such as variational autoencoders and more up-to-date models such as large language
    models and diffusion models. We also discuss how AI can have a real impact on
    chemistry, including the possibilities and problems associated with synthesizing
    AI-generated molecules.'
  description_type: abstract
  lang: und

## Creator

- name: Masato Sumita
  role: author
  orcid: https://orcid.org/0000-0002-3506-1028
- name: Shoichi Ishida
  role: author
  orcid: https://orcid.org/0000-0002-5638-3579
- name: Kazuki Yoshizoe
  role: author
  orcid: https://orcid.org/0000-0001-7715-1868
- name: Ryo Tamura
  role: author
  orcid: https://orcid.org/0000-0002-0349-358X
- name: Kei Terayama
  role: author
  orcid: https://orcid.org/0000-0003-3914-248X
- name: Koji Tsuda
  role: author
  orcid: https://orcid.org/0000-0002-4288-1606

## Contact agent



## Publisher

organization: American Chemical Society (ACS)

## Managing organization



## Keyword

- subject: Molecular Design
  schema: not_defined
- subject: AI
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by-nc-nd/4.0/

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## Journal

- title: Chemical Reviews
  issn: '00092665'
  volume: '126'
  issue: '5'
  start_page: 3007
  end_page: 3054

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## Funding

- identifier: JPMXP1020230120
  funder_name: Ministry of Education, Culture, Sports, Science and Technology
- identifier: JPMJER1903
  funder_name: Exploratory Research for Advanced Technology
- identifier: JPMJCR21O2
  funder_name: Core Research for Evolutional Science and Technology
- identifier: JPMJPR24T8
  funder_name: Precursory Research for Embryonic Science and Technology
- identifier: JP20H04251
  funder_name: Japan Society for the Promotion of Science
- identifier: JP20H05963
  funder_name: Japan Society for the Promotion of Science
- identifier: JP24H00473
  funder_name: Japan Society for the Promotion of Science
- identifier: JPMJBY24F0
  funder_name: Japan Science and Technology Agency
- identifier: JPMJFR232U
  funder_name: Fusion Oriented REsearch for disruptive Science and Technology

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## Fileset

- id: b01e5f39-7f11-4a5e-84e6-4a1bd65dcabd
  filename: cr5c00689.pdf
  content_type: application/pdf
  size: 11919586
  md5: 6f3b4f7bb88b38a32ee4f14a468aa5eb

## Thumbnail

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filename: cr5c00689.pdf