Stefan Doerr

发表

AdaptiveBandit: A multi-armed bandit framework for adaptive sampling in molecular simulations.

Gianni De Fabritiis, Stefan Doerr, Adrià Pérez, 2020, Journal of chemical theory and computation.

On-the-Fly Learning and Sampling of Ligand Binding by High-Throughput Molecular Simulations.

S. Doerr, G. De Fabritiis, G. D. Fabritiis, 2014, Journal of chemical theory and computation.

PlayMolecule Glimpse: Understanding Protein–Ligand Property Predictions with Interpretable Neural Networks

G. D. Fabritiis, S. Doerr, Maciej Majewski, 2022, J. Chem. Inf. Model..

Complete protein–protein association kinetics in atomic detail revealed by molecular dynamics simulations and Markov modelling

F. Noé, G. de Fabritiis, S. Doerr, 2017, Nature Chemistry.

DeepSite: protein‐binding site predictor using 3D‐convolutional neural networks

Gianni De Fabritiis, Alexander S. Rose, Gerard Martínez-Rosell, 2017, Bioinform..

TorchMD: A Deep Learning Framework for Molecular Simulations pdf

Toni Giorgino, Frank Noé, Cecilia Clementi, 2020, Journal of chemical theory and computation.

Kinetic Characterization of Fragment Binding in AmpC β-Lactamase by High-Throughput Molecular Simulations

Andrea Cavalli, Paola Bisignano, Matthew J. Harvey, 2014, J. Chem. Inf. Model..

Evaluation of K-Ras4B dimer interfaces and the role of Raf effectors

Z. Cournia, S. Doerr, J. Duca, 2022, bioRxiv.

Membrane Composition and Raf[CRD]-Membrane Attachment Are Driving Forces for K-Ras4B Dimer Stability.

Z. Cournia, S. Doerr, J. Duca, 2021, The journal of physical chemistry. B.

HTMD: High-Throughput Molecular Dynamics for Molecular Discovery.

Frank Noé, G De Fabritiis, S Doerr, 2016, Journal of chemical theory and computation.

High-Throughput Automated Preparation and Simulation of Membrane Proteins with HTMD.

Toni Giorgino, Gianni De Fabritiis, Gerard Martínez-Rosell, 2017, Journal of chemical theory and computation.

Dopamine D3 receptor antagonist reveals a cryptic pocket in aminergic GPCRs

Y. Zou, B. L. Kormos, G. de Fabritiis, 2018, Scientific Reports.

HTMD: High-Throughput Molecular Dynamics for Molecular Discovery.

F. Noé, M. Harvey, G. D. Fabritiis, 2016, Journal of chemical theory and computation.

NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics

J. Chodera, G. D. Fabritiis, R. Fino, 2022, J. Chem. Inf. Model..

NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics

Gianni De Fabritiis, Roberto Fino, John D. Chodera, 2022, ArXiv.

Machine learning coarse-grained potentials of protein thermodynamics

G. D. Fabritiis, C. Clementi, S. Doerr, 2022, Nature communications.

A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning

Gianni De Fabritiis, Matt J. Harvey, Stefan Doerr, 2019, J. Chem. Inf. Model..

PlayMolecule Parameterize: a Scalable Molecular Force Field Parameterization Method Based on Quantum-Level Machine Learning

M. Harvey, G. D. Fabritiis, G. de Fabritiis, 2019 .

Dimensionality reduction methods for molecular simulations pdf

Gianni De Fabritiis, Matt J. Harvey, Stefan Doerr, 2017, ArXiv.

Kinetic Characterization of Fragment Binding in AmpC β-Lactamase by High-Throughput Molecular Simulations

Angelo D. Favia, A. Cavalli, M. Harvey, 2014, J. Chem. Inf. Model..