21 datasets found

A dataset of 630 coarse-grained drug-membrane permeations is used to develop the DMInet model.

A dataset of 22.9 million atomic environments drawn from ML-driven molecular-dynamics simulations of different disordered carbon structures.

The dataset used in the paper for machine learning and excited-state molecular dynamics.

The dataset used in the paper for machine learning and excited-state molecular dynamics.

The dataset is used to test the DeePMD model for molecular dynamics simulations.

The dataset is used to train and test the DeePMD model for molecular dynamics simulations.

The eBDIMS server generates transition pathways between protein end-states, and allows for validation and rational analysis within the conformational landscape defined from...

All-atom molecular dynamics simulations of the projection domain of the intrinsically disordered htau40 protein

The dataset used in the paper is a set of molecular dynamics simulations of alanine dipeptide and met-enkephalin. The dataset contains 7 iterations of active enhanced sampling...

Molecular dynamics (MD) simulation is a widely used technique to simulate molecular systems, most commonly at the all-atom resolution where equations of motion are integrated...

The CLN025 peptide dataset is a collection of molecular dynamics trajectories of a small peptide, with size n = 3,758 and D = 32.

The dataset used in the paper is a set of molecular dynamics simulations with temperature-accelerated molecular dynamics (TAMD) and enhanced sampling methods.

The dataset used in the paper is a set of molecular dynamics simulations with temperature-accelerated molecular dynamics (TAMD) and enhanced sampling methods.

The fast-folding proteins dataset used in the paper.

The dataset used in the paper is a set of molecular dynamics simulations with temperature-accelerated molecular dynamics (TAMD) and enhanced sampling methods.

The dataset is used to train the score dynamics model.

Score dynamics is a computational method to scale up molecular dynamics simulations both spatially and temporally.

Learning neural generative dynamics for molecular conformation generation.

The dataset used in this paper is a collection of molecular dynamics trajectories of ten small molecules.

The dataset used in the paper is a simulation of a system of 4-body motion governed by a Hamiltonian model. The dataset contains 100,000 sets of positions in three dimensional...