245 citations · 520 across the 8 of their papers we have counts for
8 papers
NeuralPLexer3: Accurate Biomolecular Complex Structure Prediction with Flow Models
Zhuoran Qiao, Feizhi Ding, Thomas Dresselhaus +10
Structure determination is essential to a mechanistic understanding of diseases and the development of novel therapeutics. Machine-learning-based structure prediction methods have…
Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing
Shengchao Liu, Weili Nie, Chengpeng Wang +6
There is increasing adoption of artificial intelligence in drug discovery. However, existing studies use machine learning to mainly utilize the chemical structures of molecules but…
State-specific protein-ligand complex structure prediction with a multi-scale deep generative model
Zhuoran Qiao, Weili Nie, Arash Vahdat +2
The binding complexes formed by proteins and small molecule ligands are ubiquitous and critical to life. Despite recent advancements in protein structure prediction, existing algor…
Retrieval-based Controllable Molecule Generation
Zichao Wang, Weili Nie, Zhuoran Qiao +3
Generating new molecules with specified chemical and biological properties via generative models has emerged as a promising direction for drug discovery. However, existing methods…
OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy
Anders S. Christensen, Sai Krishna Sirumalla, Zhuoran Qiao +8
We present OrbNet Denali, a machine learning model for electronic structure that is designed as a drop-in replacement for ground-state density functional theory (DFT) energy calcul…
Informing Geometric Deep Learning with Electronic Interactions to Accelerate Quantum Chemistry
Zhuoran Qiao, Anders S. Christensen, Matthew Welborn +3
Predicting electronic energies, densities, and related chemical properties can facilitate the discovery of novel catalysts, medicines, and battery materials. By developing a physic…
Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces
Zhuoran Qiao, Feizhi Ding, Matthew Welborn +5
We refine the OrbNet model to accurately predict energy, forces, and other response properties for molecules using a graph neural-network architecture based on features from low-co…
OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features
Zhuoran Qiao, Matthew Welborn, Animashree Anandkumar +2
We introduce a machine learning method in which energy solutions from the Schrodinger equation are predicted using symmetry adapted atomic orbitals features and a graph neural-netw…