Recent Advancements in the Application of Artificial Intelligence in Drug Molecular Generation and Synthesis Planning

Buyong Ma; Yiguo Wang; Xingzi Li; Chang Shen; Hao Lin; Chenxi Du; Shanlin Yang; Ruoqing Zeng; Xuyang Tang; Jinglei Hu; Yukun Yang; Jingwen Wang; Jiawei Zhu; Xingqian Shan; Yu Zhang; Jiaqing Hu

doi:10.1055/s-0044-1796647

Pharmaceutical Fronts, Table of Contents

CC BY 4.0 · Pharmaceutical Fronts 2024; 06(04): e394-e405
DOI: 10.1055/s-0044-1796647

Review Article

Recent Advancements in the Application of Artificial Intelligence in Drug Molecular Generation and Synthesis Planning

Buyong Ma

¹Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai, People's Republic of China

,

Yiguo Wang

¹Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai, People's Republic of China

,

Xingzi Li

¹Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai, People's Republic of China

,

Chang Shen

¹Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai, People's Republic of China

,

Hao Lin

¹Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai, People's Republic of China

,

Chenxi Du

¹Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai, People's Republic of China

,

Shanlin Yang

¹Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai, People's Republic of China

,

Ruoqing Zeng

¹Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai, People's Republic of China

,

Xuyang Tang

¹Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai, People's Republic of China

,

Jinglei Hu

¹Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai, People's Republic of China

,

Yukun Yang

¹Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai, People's Republic of China

,

Jingwen Wang

¹Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai, People's Republic of China

,

Jiawei Zhu

¹Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai, People's Republic of China

,

Xingqian Shan

¹Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai, People's Republic of China

,

Yu Zhang

¹Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai, People's Republic of China

,

Jiaqing Hu

¹Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai, People's Republic of China

› Author Affiliations

Abstract

The design and synthesis of drug molecules is a pivotal stage in drug development that traditionally requires significant investment in time and finances. However, the integration of artificial intelligence (AI) in drug design accelerates the identification of potential drug candidates, optimizes the drug development process, and contributes to more informed decision-making. The application of AI in molecular generation is changing the way researchers explore the chemical space and design novel compounds. It accelerates the process of drug discovery and materials science, enabling rapid exploration of the vast chemical landscapes for the identification of promising candidates for further experimental validation. The application of AI in predicting reaction products accelerates the synthesis planning process, contributes to the automation of synthetic chemistry tasks, and supports chemists in making informed decisions during drug discovery. This paper reviewed the recent advances in two interrelated areas: the application of AI in molecular generation and synthesis routes. It will provide insights into the innovative ways in which AI is transforming traditional approaches in drug development and predict its future progress in these key fields.

Keywords

artificial intelligence - drug screen - molecular generation - retrosynthesis - deep learning

Full Text

References

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