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Matthias Rupp

Showing results (21-30 of 32) with videos related to

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The Journal of Chemical Physics|May 16, 2012
Optimizing transition states via kernel-based machine learningZachary D Pozun, Katja Hansen, Daniel Sheppard, et al.
The Journal of Chemical Physics|June 3, 2019
Chemical diversity in molecular orbital energy predictions with kernel ridge regressionAnnika Stuke, Milica Todorović, Matthias Rupp, et al.
Molecular Informatics|July 28, 2016
Target Profile Prediction: Cross-Activation of Peroxisome Proliferator-Activated Receptor (PPAR) and Farnesoid X Receptor (FXR)Ramona Steri, Petra Schneider, Alexander Klenner, et al.
Plos Computational Biology|January 24, 2014
Machine learning estimates of natural product conformational energiesMatthias Rupp, Matthias R Bauer, Rainer Wilcken, et al.
Plos One|August 6, 2011
Spherical harmonics coefficients for ligand-based virtual screening of cyclooxygenase inhibitorsQuan Wang, Kerstin Birod, Carlo Angioni, et al.
Plos Computational Biology|February 24, 2012
DOGS: reaction-driven de novo design of bioactive compoundsMarkus Hartenfeller, Heiko Zettl, Miriam Walter, et al.
Journal of Chemical Theory and Computation|November 20, 2015
Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization EnergiesKatja Hansen, Grégoire Montavon, Franziska Biegler, et al.
Bioorganic & Medicinal Chemistry Letters|March 30, 2010
Truxillic acid derivatives act as peroxisome proliferator-activated receptor gamma activatorsRamona Steri, Matthias Rupp, Ewgenij Proschak, et al.
Chemmedchem|January 1, 2010
From machine learning to natural product derivatives that selectively activate transcription factor PPARgammaMatthias Rupp, Timon Schroeter, Ramona Steri, et al.
Chemical Science|February 6, 2025
Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023Igor Poltavsky, Mirela Puleva, Anton Charkin-Gorbulin, et al.
Pageof 4

Showing results (21-30 of 32) with videos related to

Sort By:
Pageof 4
The Journal of Chemical Physics|May 16, 2012
Optimizing transition states via kernel-based machine learningZachary D Pozun, Katja Hansen, Daniel Sheppard, et al.
The Journal of Chemical Physics|June 3, 2019
Chemical diversity in molecular orbital energy predictions with kernel ridge regressionAnnika Stuke, Milica Todorović, Matthias Rupp, et al.
Molecular Informatics|July 28, 2016
Target Profile Prediction: Cross-Activation of Peroxisome Proliferator-Activated Receptor (PPAR) and Farnesoid X Receptor (FXR)Ramona Steri, Petra Schneider, Alexander Klenner, et al.
Plos Computational Biology|January 24, 2014
Machine learning estimates of natural product conformational energiesMatthias Rupp, Matthias R Bauer, Rainer Wilcken, et al.
Plos One|August 6, 2011
Spherical harmonics coefficients for ligand-based virtual screening of cyclooxygenase inhibitorsQuan Wang, Kerstin Birod, Carlo Angioni, et al.
Plos Computational Biology|February 24, 2012
DOGS: reaction-driven de novo design of bioactive compoundsMarkus Hartenfeller, Heiko Zettl, Miriam Walter, et al.
Journal of Chemical Theory and Computation|November 20, 2015
Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization EnergiesKatja Hansen, Grégoire Montavon, Franziska Biegler, et al.
Bioorganic & Medicinal Chemistry Letters|March 30, 2010
Truxillic acid derivatives act as peroxisome proliferator-activated receptor gamma activatorsRamona Steri, Matthias Rupp, Ewgenij Proschak, et al.
Chemmedchem|January 1, 2010
From machine learning to natural product derivatives that selectively activate transcription factor PPARgammaMatthias Rupp, Timon Schroeter, Ramona Steri, et al.
Chemical Science|February 6, 2025
Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023Igor Poltavsky, Mirela Puleva, Anton Charkin-Gorbulin, et al.
Pageof 4