616 citations · 688 across the 6 of their papers we have counts for
6 papers
HelixVS: Deep Learning-Enhanced Structure-Based Platform for Screening and Design
Shanzhuo Zhang, Xianbin Ye, Donglong He +3
Drug discovery through virtual screening (VS) has become a popular strategy for identifying hits against protein targets. Alongside VS, molecular design further expands accessible…
Pre-Training on Large-Scale Generated Docking Conformations with HelixDock to Unlock the Potential of Protein-ligand Structure Prediction Models
Lihang Liu, Shanzhuo Zhang, Donglong He +12
Protein-ligand structure prediction is an essential task in drug discovery, predicting the binding interactions between small molecules (ligands) and target proteins (receptors). R…
GEM-2: Next Generation Molecular Property Prediction Network by Modeling Full-range Many-body Interactions
Lihang Liu, Donglong He, Xiaomin Fang +4
Molecular property prediction is a fundamental task in the drug and material industries. Physically, the properties of a molecule are determined by its own electronic structure, wh…
HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer
Shanzhuo Zhang, Zhiyuan Yan, Yueyang Huang +8
Accurate ADMET (an abbreviation for "absorption, distribution, metabolism, excretion, and toxicity") predictions can efficiently screen out undesirable drug candidates in the early…
LiteGEM: Lite Geometry Enhanced Molecular Representation Learning for Quantum Property Prediction
Shanzhuo Zhang, Lihang Liu, Sheng Gao +6
In this report, we (SuperHelix team) present our solution to KDD Cup 2021-PCQM4M-LSC, a large-scale quantum chemistry dataset on predicting HOMO-LUMO gap of molecules. Our solution…
ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction
Xiaomin Fang, Lihang Liu, Jieqiong Lei +6
Effective molecular representation learning is of great importance to facilitate molecular property prediction, which is a fundamental task for the drug and material industry. Rece…