TamGen: drug design with target-aware molecule generation through a chemical language model

Kehan Wu, Yingce Xia (), Pan Deng, Renhe Liu, Yuan Zhang, Han Guo, Yumeng Cui, Qizhi Pei, Lijun Wu, Shufang Xie, Si Chen, Xi Lu, Song Hu, Jinzhi Wu, Chi-Kin Chan, Shawn Chen, Liangliang Zhou, Nenghai Yu, Enhong Chen, Haiguang Liu, Jinjiang Guo (), Tao Qin () and Tie-Yan Liu
Additional contact information
Kehan Wu: University of Science and Technology of China
Yingce Xia: Microsoft Research AI for Science
Pan Deng: Microsoft Research AI for Science
Renhe Liu: Global Health Drug Discovery Institute
Yuan Zhang: Global Health Drug Discovery Institute
Han Guo: Global Health Drug Discovery Institute
Yumeng Cui: Global Health Drug Discovery Institute
Qizhi Pei: Renmin University of China
Lijun Wu: Microsoft Research AI for Science
Shufang Xie: Microsoft Research AI for Science
Si Chen: Global Health Drug Discovery Institute
Xi Lu: Global Health Drug Discovery Institute
Song Hu: Global Health Drug Discovery Institute
Jinzhi Wu: Global Health Drug Discovery Institute
Chi-Kin Chan: Global Health Drug Discovery Institute
Shawn Chen: Global Health Drug Discovery Institute
Liangliang Zhou: Global Health Drug Discovery Institute
Nenghai Yu: University of Science and Technology of China
Enhong Chen: University of Science and Technology of China
Haiguang Liu: Microsoft Research AI for Science
Jinjiang Guo: Global Health Drug Discovery Institute
Tao Qin: Microsoft Research AI for Science
Tie-Yan Liu: Microsoft Research AI for Science

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract Generative drug design facilitates the creation of compounds effective against pathogenic target proteins. This opens up the potential to discover novel compounds within the vast chemical space and fosters the development of innovative therapeutic strategies. However, the practicality of generated molecules is often limited, as many designs focus on a narrow set of drug-related properties, failing to improve the success rate of subsequent drug discovery process. To overcome these challenges, we develop TamGen, a method that employs a GPT-like chemical language model and enables target-aware molecule generation and compound refinement. We demonstrate that the compounds generated by TamGen have improved molecular quality and viability. Additionally, we have integrated TamGen into a drug discovery pipeline and identified 14 compounds showing compelling inhibitory activity against the Tuberculosis ClpP protease, with the most effective compound exhibiting a half maximal inhibitory concentration (IC50) of 1.9 μM. Our findings underscore the practical potential and real-world applicability of generative drug design approaches, paving the way for future advancements in the field.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-53632-4 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-53632-4

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-53632-4

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().