Artificial Intelligence and Big Data in Chemistry: From Molecular Data to Autonomous Discovery

Artificial Intelligence and Big Data in Chemistry: From Molecular Data to Autonomous Discovery Научная публикация

Журнал

Chemistry-Switzerland
ISSN: 2624-8549

Вых. Данные

Год: 2026, Том: 8, Номер: 11, Номер статьи : 147, Страниц : 14 DOI: 10.3390/chemistry8110147

Ключевые слова

artificial intelligence; big data; machine learning; chemical data; molecular representation; reaction informatics; autonomous laboratory; generative chemistry; large language model; FAIR data

Авторы

Ananikov Valentine P. ¹ , Li Jun ^2,3 , Zhu Tong ⁴

Организации

1	Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky Prospekt 47, 119991 Moscow, Russia
2	Chongqing College of Artificial Intelligence, Chongqing 400065, China
3	School of Chemistry and Chemical Engineering, Chongqing University, Chongqing 401331, China
4	Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China

Artificial intelligence and big data are transforming chemistry: the field is shifting from a discipline in which observations are mainly interpreted after experiments into a domain in which data, models, and experimental research form a unified, interconnected system of scientific discovery. This Perspective defines the chemical data-to-knowledge cycle and examines molecular representation, predictive modeling, synthesis planning, drug and materials discovery, large language models, and autonomous (self-driving) laboratories. The central message is that progress will depend not so much on the nominal size of a dataset or model as on chemical meaning, data provenance, uncertainty estimation, experimental validation, and the ability to convert computational results into reproducible knowledge.

Ananikov V.P. , Li J. , Zhu T.
Artificial Intelligence and Big Data in Chemistry: From Molecular Data to Autonomous Discovery
Chemistry-Switzerland. 2026. V.8. N11. 147 :1-14. DOI: 10.3390/chemistry8110147

Поступила в редакцию:	27 авг. 2026 г.
Принята к публикации:	23 окт. 2026 г.
Опубликована в печати:	27 окт. 2026 г.
Опубликована online:	27 окт. 2026 г.

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