M. Rupp

发表

Fast and accurate modeling of molecular atomization energies with machine learning.

K. Müller, M. Rupp, A. Tkatchenko, 2011, Physical review letters.

Quantum chemistry structures and properties of 134 kilo molecules

Pavlo O. Dral, M. Rupp, O. A. von Lilienfeld, 2014, Scientific Data.

Pharmacophore Alignment Search Tool (PhAST): Significance Assessment of Chemical Similarity

M. Rupp, G. Schneider, A. Hartmann, 2013, Molecular informatics.

Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties

M. Rupp, R. Ramakrishnan, O. A. V. Lilienfeld, 2013, 1307.2918.

Machine learning of molecular electronic properties in chemical compound space

M. Rupp, A. Tkatchenko, G. Montavon, 2013, 1305.7074.

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach.

Pavlo O. Dral, M. Rupp, R. Ramakrishnan, 2015, Journal of chemical theory and computation.

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies.

Klaus-Robert Müller, Siamac Fazli, Grégoire Montavon, 2013, Journal of chemical theory and computation.

Ruppet al.Reply

K. Müller, M. Rupp, A. Tkatchenko, 2012 .

Orbital-free bond breaking via machine learning.

John C. Snyder, K. Müller, M. Rupp, 2013, The Journal of chemical physics.

Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

M. Rupp, O. A. von Lilienfeld, R. Ramakrishnan, 2015, 1505.00350.

Chemical diversity in molecular orbital energy predictions with kernel ridge regression.

M. Rupp, P. Rinke, A. Stuke, 2018, The Journal of chemical physics.

Learning Invariant Representations of Molecules for Atomization Energy Prediction

Andreas Ziehe, Klaus-Robert Müller, Siamac Fazli, 2012, NIPS.

Machine-learned multi-system surrogate models for materials prediction

Conrad W. Rosenbrock, Tim Mueller, Matthias Rupp, 2018, npj Computational Materials.

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach.

Matthias Rupp, Pavlo O. Dral, Pavlo O Dral, 2015, Journal of chemical theory and computation.

Pharmacophore alignment search tool: Influence of canonical atom labeling on similarity searching

Friedrich Rippmann, Gisbert Schneider, Matthias Rupp, 2010, J. Comput. Chem..

Predicting the pKa of Small Molecules

I. Tetko, M. Rupp, R. Körner, 2011 .

Spherical Harmonics Coefficients for Ligand-Based Virtual Screening of Cyclooxygenase Inhibitors

Petra Schneider, Gisbert Schneider, Matthias Rupp, 2011, PloS one.

From Machine Learning to Natural Product Derivatives that Selectively Activate Transcription Factor PPARγ

Klaus-Robert Müller, Gisbert Schneider, Matthias Rupp, 2010, ChemMedChem.

Predicting the p Ka of Small Molecules

I. Tetko, M. Rupp, R. Körner, 2011 .

Impact of X‐Ray Structure on Predictivity of Scoring Functions: PPARγ Case Study

M. Rupp, E. Proschak, J. Weber, 2012, Molecular informatics.

General machine-learning surrogate models for materials prediction

Conrad W. Rosenbrock, M. Rupp, D. Wingate, 2018, 1809.09203.

Identifying domains of applicability of machine learning models for materials science

M. Rupp, M. Scheffler, L. Ghiringhelli, 2019, Nature Communications.

Understanding Kernel Ridge Regression: Common behaviors from simple functions to density functionals pdf

Li Li, Klaus-Robert Müller, Kevin Vu, 2015, ArXiv.

Machine learning for quantum mechanics in a nutshell

Matthias Rupp, M. Rupp, 2015 .

Understanding Machine-learned Density Functionals pdf

Li Li, Klaus-Robert Müller, John C. Snyder, 2014, ArXiv.

Optimizing transition states via kernel-based machine learning.

Klaus-Robert Müller, Matthias Rupp, Graeme Henkelman, 2012, The Journal of chemical physics.

Unified representation of molecules and crystals for machine learning

Matthias Rupp, Haoyan Huo, M. Rupp, 2017, Mach. Learn. Sci. Technol..

Assessing the frontier: Active learning, model accuracy, and multi-objective candidate discovery and optimization.

Julia Ling, Zachary Del Rosario, Matthias Rupp, 2020, The Journal of chemical physics.

Finding Density Functionals with Machine Learning

Klaus-Robert Müller, John C. Snyder, Matthias Rupp, 2011, Physical review letters.

Machine Learning Estimates of Natural Product Conformational Energies

Gisbert Schneider, Matthias Rupp, Frank M. Boeckler, 2014, PLoS Comput. Biol..

Heat flux for semi-local machine-learning potentials

M. Rupp, M. Scheffler, Christian Carbogno, 2023, ArXiv.

Multi-task learning for pKa prediction

Guido Sanguinetti, Matthias Rupp, Grigorios Skolidis, 2012, Journal of Computer-Aided Molecular Design.

Estimation of Acid Dissociation Constants Using Graph Kernels

Igor V Tetko, Matthias Rupp, Robert Körner, 2010, Molecular informatics.

Kernel Approach to Molecular Similarity Based on Iterative Graph Similarity

Gisbert Schneider, Matthias Rupp, Ewgenij Proschak, 2007, J. Chem. Inf. Model..

Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information

Igor V. Tetko, Antony J. Williams, Artem Cherkasov, 2011, J. Comput. Aided Mol. Des..

Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning

M. Rupp, Alex Goessmann, Marcel F. Langer, 2020, npj Computational Materials.

Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry.

Kieron Burke, Matthias Rupp, O Anatole von Lilienfeld, 2018, The Journal of chemical physics.

DOGS: Reaction-Driven de novo Design of Bioactive Compounds

Markus Hartenfeller, Gisbert Schneider, Matthias Rupp, 2012, PLoS Comput. Biol..

Assessing the Frontier: Active Learning, Model Accuracy, and Multi-objective Materials Discovery and Optimization

M. Rupp, Julia Ling, Erin Antono, 2019, ArXiv.

Distance phenomena in high‐dimensional chemical descriptor spaces: Consequences for similarity‐based approaches

Petra Schneider, Gisbert Schneider, Matthias Rupp, 2009, J. Comput. Chem..

Ultra-fast interpretable machine-learning potentials

M. Rupp, R. Hennig, S. Xie, 2021, npj Computational Materials.

Topic on Data-enabled Theoretical Chemistry

M. Rupp, K. Burke, O. A. V. Lilienfeld, 2018 .

Special issue on machine learning and quantum mechanics

Matthias Rupp, M. Rupp, 2015 .

Graph Kernels for Molecular Similarity

Gisbert Schneider, Matthias Rupp, M. Rupp, 2010, Molecular informatics.

Virtual screening for PPAR-gamma ligands using the ISOAK molecular graph kernel and gaussian processes

K-R Müller, T Schroeter, M Rupp, 2009 .

Shapelets: Possibilities and limitations of shape‐based virtual screening

Gisbert Schneider, Matthias Rupp, Ewgenij Proschak, 2008, J. Comput. Chem..

Truxillic acid derivatives act as peroxisome proliferator-activated receptor gamma activators.

K. Müller, M. Rupp, G. Schneider, 2010, Bioorganic & medicinal chemistry letters.

Chemical diversity in molecular orbital energy predictions with kernel ridge regression.

M. Rupp, P. Rinke, A. Stuke, 2018, The Journal of chemical physics.

Predicting the pKa of small molecule.

I. Tetko, M. Rupp, R. Körner, 2011, Combinatorial chemistry & high throughput screening.

Kernel learning for ligand-based virtual screening: discovery of a new PPARγ agonist

K. Müller, M. Rupp, G. Schneider, 2010, J. Cheminformatics.