2 citations · 4 across the 5 of their papers we have counts for
5 papers
A Massively Scalable Ligand-Protein Dissociation Dynamic Database Derived from Atomistic Molecular Modelling
Maodong Li, Dechin Chen, Zhijun Pan +2
Understanding the kinetics of drug-protein interactions is paramount for drug design, yet the field lacks large-scale, dynamic data to move beyond static structural analysis. Here,…
BiScale-GTR: Fragment-Aware Graph Transformers for Multi-Scale Molecular Representation Learning
Yi Yang, Ovidiu Daescu
Fragment-level representations provide a natural way to capture recurring molecular substructures and reuse their learned representations across molecules. However, a shared fragme…
Open Molecular Crystals 2025 (OMC25) Dataset and Models
Vahe Gharakhanyan, Luis Barroso-Luque, Yi Yang +16
The development of accurate and efficient machine learning models for predicting the structure and properties of molecular crystals has been hindered by the scarcity of publicly av…
FastCSP: Accelerated Molecular Crystal Structure Prediction with Universal Model for Atoms
Vahe Gharakhanyan, Yi Yang, Luis Barroso-Luque +24
Molecular crystal structure prediction (CSP) is essential for applications in pharmaceuticals and organic electronics. However, CSP remains challenging and computationally intensiv…
Improving Protein Sequence Design through Designability Preference Optimization
Fanglei Xue, Andrew Kubaney, Zhichun Guo +4
Protein sequence design methods have demonstrated strong performance in sequence generation for de novo protein design. However, as the training objective was sequence recovery, it…