33 citations · 59 across the 9 of their papers we have counts for
9 papers
Auto-WHATMD : Automated Wasserstein-based High-dimensional feature extraction Analysis of Trajectories from Molecular Dynamics
Sosuke Asano, Ikki Yasuda, Katsuhiro Endo +2
Comparing multiple protein systems with variation such as different binding ligands or mutations, and understanding their effects is one of the objectives in molecular dynamics sim…
Navigating Phase Transitions with Path-Finding Algorithms: A Strategic Approach to Replica Exchange Monte Carlo
Akie Kowaguchi, Katsuhiro Endo, Kentaro Nomura +3
The replica exchange method is a powerful tool for overcoming slow relaxation in molecular simulations, but its efficiency depends strongly on the choice of the number and interval…
Optimal Parameter Configurations for Sequential Optimization of Variational Quantum Eigensolver
Katsuhiro Endo, Yuki Sato, Rudy Raymond +3
Variational Quantum Eigensolver (VQE) is a hybrid algorithm for finding the minimum eigenvalue/vector of a given Hamiltonian by optimizing a parametrized quantum circuit (PQC) usin…
Variational quantum algorithm for generalized eigenvalue problems and its application to the finite element method
Yuki Sato, Hiroshi C. Watanabe, Rudy Raymond +5
Generalized eigenvalue problems (GEPs) play an important role in the variety of fields including engineering, machine learning and quantum chemistry. Especially, many problems in t…
Prediction of transport property via machine learning molecular movements
Ikki Yasuda, Yusei Kobayashi, Katsuhiro Endo +5
Molecular dynamics (MD) simulations are increasingly being combined with machine learning (ML) to predict material properties. The molecular configurations obtained from MD are rep…
MD-GAN with multi-particle input: the machine learning of long-time molecular behavior from short-time MD data
Ryo Kawada, Katsuhiro Endo, Daisuke Yuhara +1
MD-GAN is a machine learning-based method that can evolve part of the system at any time step, accelerating the generation of molecular dynamics data. For the accurate prediction o…
Ligand-induced protein dynamics differences correlate with protein-ligand binding affinities: An unsupervised deep learning approach
Ikki Yasuda, Katsuhiro Endo, Eiji Yamamoto +2
Prediction of protein-ligand binding affinity is a major goal in drug discovery. Generally, free energy gap is calculated between two states (e.g., ligand binding and unbinding). T…
Quantum self-learning Monte Carlo with quantum Fourier transform sampler
Katsuhiro Endo, Taichi Nakamura, Keisuke Fujii +1
The self-learning Metropolis-Hastings algorithm is a powerful Monte Carlo method that, with the help of machine learning, adaptively generates an easy-to-sample probability distrib…
Molecular Flow Monte Carlo
Katsuhiro Endo, Daisuke Yuhara, Kenji Yasuoka
In this paper, we suggest a novel sampling method for Monte Carlo molecular simulations. In order to perform efficient sampling of molecular systems, it is advantageous to avoid ex…