5 papers
PCEvo: Path-Consistent Molecular Representation via Virtual Evolutionary
Kun Li, Longtao Hu, Yida Xiong +6
Molecular representation learning aims to learn vector embeddings that capture molecular structure and geometry, thereby enabling property prediction and downstream scientific appl…
FP-AbDiff: Improving Score-based Antibody Design by Capturing Nonequilibrium Dynamics through the Underlying Fokker-Planck Equation
Jiameng Chen, Yida Xiong, Kun Li +4
Computational antibody design holds immense promise for therapeutic discovery, yet existing generative models are fundamentally limited by two core challenges: (i) a lack of dynami…
Transport-Coupled Bayesian Flows for Molecular Graph Generation
Yida Xiong, Jiameng Chen, Kun Li +4
Molecular graph generation (MGG) is essentially a multi-class generative task, aimed at predicting categories of atoms and bonds under strict chemical and structural constraints. H…
BSL: A Unified and Generalizable Multitask Learning Platform for Virtual Drug Discovery from Design to Synthesis
Kun Li, Zhennan Wu, Yida Xiong +8
Drug discovery is of great social significance in safeguarding human health, prolonging life, and addressing the challenges of major diseases. In recent years, artificial intellige…
Can Molecular Evolution Mechanism Enhance Molecular Representation?
Kun Li, Longtao Hu, Xiantao Cai +2
Molecular evolution is the process of simulating the natural evolution of molecules in chemical space to explore potential molecular structures and properties. The relationships be…