74 citations · 382 across the 17 of their papers we have counts for
18 papers
Generating stable molecules using imitation and reinforcement learning
Søren Ager Meldgaard, Jonas Köhler, Henrik Lund Mortensen +3
Chemical space is routinely explored by machine learning methods to discover interesting molecules, before time-consuming experimental synthesizing is attempted. However, these met…
Machine Learning Implicit Solvation for Molecular Dynamics
Yaoyi Chen, Andreas Krämer, Nicholas E. Charron +3
Accurate modeling of the solvent environment for biological molecules is crucial for computational biology and drug design. A popular approach to achieve long simulation time scale…
Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning
Robin Winter, Frank Noé, Djork-Arné Clevert
Recently, there has been great success in applying deep neural networks on graph structured data. Most work, however, focuses on either node- or graph-level supervised learning, su…
Parameterized Hypercomplex Graph Neural Networks for Graph Classification
Tuan Le, Marco Bertolini, Frank Noé +1
Despite recent advances in representation learning in hypercomplex (HC) space, this subject is still vastly unexplored in the context of graphs. Motivated by the complex and quater…
Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry
Stefan Klus, Patrick Gelß, Feliks Nüske +1
We derive symmetric and antisymmetric kernels by symmetrizing and antisymmetrizing conventional kernels and analyze their properties. In particular, we compute the feature space di…
Multiscale molecular kinetics by coupling Markov state models and reaction-diffusion dynamics
Mauricio J. del Razo, Manuel Dibak, Christof Schütte +1
A novel approach to simulate simple protein-ligand systems at large time- and length-scales is to couple Markov state models (MSMs) of molecular kinetics with particle-based reacti…