Yonghong Tian

ProAR generates molecular dynamics trajectories with reduced error and improved conformational accuracy compared to existing methods.

MolSpecFlow bridges the gap between molecules and mass spectra using a hybrid flow matching model, achieving state-of-the-art results in de novo generation and spectral simulation.

SAKPE predicts enzyme kinetic parameters with improved accuracy by incorporating weighted protein representations, outperforming existing methods in kcat, Km, and Ki predictions.

This paper introduces S3AI, an interpretable method for predicting antibody-antigen interactions by combining structural knowledge and chemical rules, outperforming existing models and identifying key CDR regions.

E2VD predicts virus variation drivers using an evolution-driven framework, outperforming existing methods and identifying critical mutations for viral survival and risk.

This paper introduces an RNA language model based on multiple sequence alignment, showing it can accurately predict RNA structure from sequence data.

This paper introduces a GPU-based framework that accelerates neuron simulations and applies it to neuroscience and AI tasks, showing significant speed improvements over traditional methods.

This paper develops an AI-based simulation method to predict future high-risk SARS-CoV-2 variants by simulating RBD mutations and screening potential variants efficiently.

This paper develops a computational model of retinal gap junctions (G-filter) that improves deep neural networks' performance in blind denoising tasks.

This study investigates neural computations in foveal and peripheral visual fields during active search, finding that foveal attentional enhancements play a critical role in fixation behavior and global attention allocation.