ClickGen: Directed exploration of synthesizable chemical space via modular reactions and reinforcement learning

Mingyang Wang, Shuai Li, Jike Wang, Odin Zhang, Hongyan Du, Dejun Jiang, Zhenxing Wu, Yafeng Deng, Yu Kang, Peichen Pan, Dan Li, Xiaorui Wang, Xiaojun Yao, Tingjun Hou () and Chang-Yu Hsieh ()
Additional contact information
Mingyang Wang: Zhejiang University
Shuai Li: Chengde
Jike Wang: Zhejiang University
Odin Zhang: Zhejiang University
Hongyan Du: Zhejiang University
Dejun Jiang: Zhejiang University
Zhenxing Wu: Zhejiang University
Yafeng Deng: Chengde
Yu Kang: Zhejiang University
Peichen Pan: Zhejiang University
Dan Li: Zhejiang University
Xiaorui Wang: Macau University of Science and Technology
Xiaojun Yao: Macao Polytechnic University
Tingjun Hou: Zhejiang University
Chang-Yu Hsieh: Zhejiang University

Nature Communications, 2024, vol. 15, issue 1, 1-18

Abstract: Abstract Despite the significant potential of generative models, low synthesizability of many generated molecules limits their real-world applications. In response to this issue, we develop ClickGen, a deep learning model that utilizes modular reactions like click chemistry to assemble molecules and incorporates reinforcement learning along with inpainting technique to ensure that the proposed molecules display high diversity, novelty and strong binding tendency. ClickGen demonstrates superior performance over the other reaction-based generative models in terms of novelty, synthesizability, and docking conformation similarity for existing binders targeting the three proteins. We then proceeded to conduct wet-lab validation on the ClickGen’s proposed molecules for poly adenosine diphosphate-ribose polymerase 1. Due to the guaranteed high synthesizability and model-generated synthetic routes for reference, we successfully produced and tested the bioactivity of these novel compounds in just 20 days, much faster than typically expected time frame when handling sufficiently novel molecules. In bioactivity assays, two lead compounds demonstrated superior anti-proliferative efficacy against cancer cell lines, low toxicity, and nanomolar-level inhibitory activity to PARP1. We demonstrate that ClickGen and related models may represent a new paradigm in molecular generation, bringing AI-driven, automated experimentation and closed-loop molecular design closer to realization.

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

Downloads: (external link)
https://www.nature.com/articles/s41467-024-54456-y 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_s41467-024-54456-y

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

DOI: 10.1038/s41467-024-54456-y

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 ().