11/02/2026
๐๐๐๐ซ๐ง๐๐ ๐๐จ๐ง๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐จ๐ง๐๐ฅ ๐๐ฉ๐๐๐ ๐๐ง๐ ๐๐ก๐๐ซ๐ฆ๐๐๐จ๐ฉ๐ก๐จ๐ซ๐ ๐ข๐ง๐ญ๐จ ๐๐จ๐ฅ๐๐๐ฎ๐ฅ๐๐ซ ๐
๐จ๐ฎ๐ง๐๐๐ญ๐ข๐จ๐ง๐๐ฅ ๐๐จ๐๐๐ฅ
A key challenge in AI-driven drug discovery is moving beyond molecular analysis to enable chemically meaningful molecular design. In a newly published study in Advanced Science, researchers led by ๐บ๐๐๐๐๐ ๐ท๐๐๐๐๐๐๐๐ ๐ฐ๐๐๐๐๐๐๐๐๐๐๐, ๐ท๐๐๐. ๐๐๐๐ ๐๐๐๐๐ and collaborators from Institute of Systems Medicine, Chinese Academy of Medical Sciences, present ๐ถ๐๐๐๐๐๐๐๐, a molecular foundation model that bridges this gap by integrating molecular understanding and generation within a unified framework.
Ouroboros introduces a chemically grounded representation space that captures both the dynamic three-dimensional behaviour of drug molecules and their pharmacophoric similaritiesโfeatures often overlooked by existing AI models. By incorporating conformational-space pharmacophore similarity as a learning signal, the model recognises pharmacologically relevant relationships even among molecules with distinct chemical scaffolds.
Crucially, Ouroboros pioneers a reconstruction learning paradigm that allows molecular representations to be translated back into complete chemical structures. This capability enables direct molecular evolution and optimisation within the learned representation space, effectively โclosing the loopโ between analysis and design.
The framework demonstrates strong performance across a wide range of downstream tasks, including similarity-based virtual screening, multi-target drug design, ADMET prediction, and directed molecular optimisation. By reducing trial-and-error and improving early candidate selection, Ouroboros has the potential to shorten drug discovery timelines and lower experimental costs.
Looking ahead, the team is extending Ouroboros toward target-guided molecular generation and genome-scale drug discovery, further broadening its impact across therapeutic research.
This work was published in Advanced Science on 4th January 2026:
https://doi.org/10.1002/advs.202513556
