TorchMD: A Deep Learning Framework for Molecular Simulations-Reference-Cited by-同舟云学术

TorchMD: A Deep Learning Framework for Molecular Simulations

Published:2021-03-17 Issue:4 Volume:17 Page:2355-2363
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Doerr Stefan¹^ORCID,Majewski Maciej²^ORCID,Pérez Adrià²^ORCID,Krämer Andreas³^ORCID,Clementi Cecilia⁴⁵^ORCID,Noe Frank³⁴⁵,Giorgino Toni⁶⁷^ORCID,De Fabritiis Gianni¹²⁸^ORCID

Affiliation:

1. Acellera, 08005 Barcelona, Spain

2. Computational Science Laboratory, Universitat Pompeu Fabra, 08003 Barcelona, Spain

3. Department of Mathematics and Computer Science, Freie Universität, 14195 Berlin, Germany

4. Department of Physics, Freie Universität, 14195 Berlin, Germany

5. Department of Chemistry, Rice University, Houston, 77005 Texas, United States

6. Biophysics Institute, National Research Council (CNR-IBF), 20133 Milano, Italy

7. Department of Biosciences, Università degli Studi di Milano, 20133 Milano, Italy

8. Institució Catalana de Recerca i Estudis Avançats, 08010 Barcelona, Spain

Funder

Chan Zuckerberg Initiative

Horizon 2020 Framework Programme

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.0c01343

Reference54 articles.

1. Discovery Through the Computational Microscope

2. Force Fields for Protein Simulations

3. Drug Discovery and Molecular Dynamics: Methods, Applications and Perspective Beyond the Second Timescale

4. SchNet – A deep learning architecture for molecules and materials

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