Qian Shi — accepted AI-conference papers

2026

EvoEGF-Mol: Evolving Exponential Geodesic Flow for Structure-based Drug Design

Yaowei Jin, Junjie Wang, Cheng Cao, Penglei Wang, Duo An, Qian Shi

ICML 2026poster

Structure-Based Drug Design (SBDD) aims to discover bioactive ligands. Conventional approaches construct probability paths separately in Euclidean and probabilistic spaces for continuous atomic coordinates and discrete chemical categories, leading to a mismatch with the underlying statistical manifo…

Cited by 0 Source ↗Scholar ↗

2023

Incorporating Reliability in Graph Information Propagation by Fluid Dynamics Diffusion: A case of Multimodal Semisupervised Deep Learning

Andrea Marinoni, Marine Mercier, Qian Shi, Sivasakthy Selvakumaran, Mark Girolami

ICASSP 2023accepted

Classic graph neural networks show some limitations in information extraction performance when applied to multimodal datasets. This is primarily due to such datasets having high volume, variety, and variability. In this paper, we propose structuring graph neural networks on a new graph representatio…

Cited by 0 Source ↗Scholar ↗