Search Results for author:
Found 8 papers, 5 papers with code
Brain-inspired Chaotic Graph Backpropagation for Large-scale Combinatorial Optimization
To address this issue, inspired by possibly chaotic dynamics of real brain learning, we introduce a chaotic training algorithm, i. e. chaotic graph backpropagation (CGBP), which introduces a local loss function in GNN that makes the training process not only chaotic but also highly efficient.
The need to implement FAIR principles in biomolecular simulations
This letter illustrates the opinion of the molecular dynamics (MD) community on the need to adopt a new FAIR paradigm for the use of molecular simulations.
PASSerRank: Prediction of Allosteric Sites with Learning to Rank
One of the major challenges in allosteric drug research is the identification of allosteric sites.
LAST: Latent Space Assisted Adaptive Sampling for Protein Trajectories
Based on this characteristic, we proposed a new adaptive sampling method, latent space assisted adaptive sampling for protein trajectories (LAST), to accelerate the exploration of protein conformational space.
Time Series Prediction by Multi-task GPR with Spatiotemporal Information Transformation
Making an accurate prediction of an unknown system only from a short-term time series is difficult due to the lack of sufficient information, especially in a multi-step-ahead manner.
Accurate ADMET Prediction with XGBoost
The absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties are important in drug discovery as they define efficacy and safety.
Ranked #2 on
TDC ADMET Benchmarking Group
on tdcommons
ivis Dimensionality Reduction Framework for Biomacromolecular Simulations
Molecular dynamics (MD) simulations have been widely applied to study macromolecules including proteins.
Residual-Recursion Autoencoder for Shape Illustration Images
Shape illustration images (SIIs) are common and important in describing the cross-sections of industrial products.
