Machine Learning for Biomolecular Design and Simulation
FOCUS · C04 · ID: 381261
Presentations
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Rational optimization of drug-membrane selectivity by computational screening
ORAL
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Presenters
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Bernadette Mohr
Van ‘t Hoff Institute for Molecular Sciences, Informatics Institute, University of Amsterdam
Authors
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Bernadette Mohr
Van ‘t Hoff Institute for Molecular Sciences, Informatics Institute, University of Amsterdam
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Kirill Shmilovich
Pritzker School of Molecular Engineering, University of Chicago
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Tristan Bereau
University of Amsterdam, Van 't Hoff Institute for Molecular Sciences and Informatics Institute, University of Amsterdam, Van ‘t Hoff Institute for Molecular Sciences, Informatics Institute, University of Amsterdam
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Andrew Ferguson
University of Chicago, Pritzker School of Molecular Engineering, University of Chicago
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Learning molecular models from simulation and experimental data
Invited
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Presenters
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Cecilia Clementi
Rice Univ, Physics, Freie Universität Berlin
Authors
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Cecilia Clementi
Rice Univ, Physics, Freie Universität Berlin
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Toward Transferable Deep Learning Atomistic Potential for Biomolecular Simulations
ORAL
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Presenters
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Olexandr Isayev
Carnegie Mellon Univ
Authors
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Olexandr Isayev
Carnegie Mellon Univ
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Accurate Molecular Polarizabilities with Coupled Cluster Theory and Machine Learning
ORAL
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Presenters
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Yang Yang
Chemistry and Chemical Biology, Cornell University, Department of Chemistry and Chemical Biology, Cornell University, Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY
Authors
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Yang Yang
Chemistry and Chemical Biology, Cornell University, Department of Chemistry and Chemical Biology, Cornell University, Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY
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Ka Un Lao
Department of Chemistry and Chemical Biology, Cornell University
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David M. Wilkins
Laboratory of Computational Science and Modeling, Institut des Matériaux, École Polytechnique Fédérale de Lausanne
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Andrea Grisafi
École Polytechnique Federale de Lausanne, Laboratory of Computational Science and Modeling, Institut des Matériaux, École Polytechnique Fédérale de Lausanne
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Michele Ceriotti
Ecole polytechnique federale de Lausanne, Ecole Polytechnique Federale de Lausanne, Institute of Materials, Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland, École Polytechnique Federale de Lausanne, Laboratory of Computational Science and Modeling, Institut des Matériaux, École Polytechnique Fédérale de Lausanne
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Robert Distasio
Chemistry and Chemical Biology, Cornell University, Department of Chemistry and Chemical Biology, Cornell University, Cornell University, Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY
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Machine Learning on a Quantum Hamiltonian shows that DNA is Much Stretchier than Classical Simulations Suggest
ORAL
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Presenters
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Joshua Berryman
University of Luxembourg Limpertsberg
Authors
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Joshua Berryman
University of Luxembourg Limpertsberg
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Machine learning for DNA self-assembly: a numerical case study
ORAL
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Presenters
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Jörn Appeldorn
Institute of Physics, Johannes Gutenberg University Mainz
Authors
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Jörn Appeldorn
Institute of Physics, Johannes Gutenberg University Mainz
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Arash Nikoubashman
University of Mainz, Department of Physics, University of Mainz, Johannes Gutenberg University, Institute of Physics, Johannes Gutenberg University Mainz
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Thomas Speck
Institute of Physics, Johannes Gutenberg University Mainz
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Predicting Protein Developability via Convolutional Sequence Representation
ORAL
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Presenters
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Alexander Golinski
University of Minnesota, Department of Chemical Engineering and Materials Science, University of Minnnesota
Authors
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Alexander Golinski
University of Minnesota, Department of Chemical Engineering and Materials Science, University of Minnnesota
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Bryce Johnson
University of Minnesota, School of Physics and Astronomy, University of Minnesota
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Sidharth Laxminarayan
University of Minnesota
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Diya Saha
University of Minnesota, Department of Chemical Engineering and Materials Science, University of Minnnesota
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Sandhya Appiah
University of Minnesota, Department of Chemical Engineering and Materials Science, University of Minnnesota
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Benjamin Hackel
University of Minnesota
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Stefano Martiniani
University of Minnesota, Chemical Engineering and Materials Science, University of Minnesota, Department of Chemical Engineering and Materials Science, University of Minnesota, Department of Chemical Engineering and Materials Science, University of Minnnesota
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Supremum modeling to extend model transferability in systems biology
ORAL
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Presenters
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Cody Petrie
Brigham Young University
Authors
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Cody Petrie
Brigham Young University
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Christian Anderson
Brigham Young University
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Mark Transtrum
Physics and Astronomy, Brigham Young University, Brigham Young University
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Prospective experimental validation of machine learning for biological sequence design
Invited
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Presenters
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Lucy Colwell
Univ of Cambridge
Authors
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Lucy Colwell
Univ of Cambridge
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Recurrent networks for protein structure prediction using Frenet-Serret equations and latent residue representations
ORAL
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Presenters
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Nazim Bouatta
Harvard Medical School
Authors
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Nazim Bouatta
Harvard Medical School
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Multi-fidelity integrated computational-experimental design of self-assembling π-conjugated optoelectronic peptides
ORAL
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Presenters
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Kirill Shmilovich
University of Chicago
Authors
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Kirill Shmilovich
University of Chicago
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Sayak Panda
Johns Hopkins University
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John D. Tovar
Johns Hopkins University
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Andrew Ferguson
University of Chicago, Pritzker School of Molecular Engineering, University of Chicago
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