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Publication details

Document type
Journal articles

Document subtype
Full paper

Title
A Fast and Interpretable Deep Learning Approach for Accurate Electrostatics-Driven pKa Predictions in Proteins

Participants in the publication
Pedro B.P.S. Reis (Author)
Marco Bertolini (Author)
Floriane Montanari (Author)
Walter Rocchia (Author)
Miguel Machuqueiro (Author)
Dep. Química e Bioquímica
BioISI
Djork-Arné Clevert (Author)

Date of Publication
2022-07-15

Institution
FACULDADE DE CIÊNCIAS DA UNIVERSIDADE DE LISBOA

Where published
Journal of Chemical Theory and Computation

Publication Identifiers
ISSN - 1549-9618
eISSN - 1549-9626

Publisher
American Chemical Society (ACS)

Volume
18
Number
8

Number of pages
11
Starting page
5068
Last page
5078

Document Identifiers
DOI - https://doi.org/10.1021/acs.jctc.2c00308
URL - http://dx.doi.org/10.1021/acs.jctc.2c00308

Rankings
SCIMAGO Q1 (2021) - 1619 - Physical and Theoretical Chemistry
Web Of Science Q1 (2021) - 6.578 - PHYSICS, ATOMIC, MOLECULAR & CHEMICAL - SCIE
SCOPUS Q1 (2021) - 10 - Physical and Theoretical Chemistry


Export

APA
Pedro B.P.S. Reis, Marco Bertolini, Floriane Montanari, Walter Rocchia, Miguel Machuqueiro, Djork-Arné Clevert, (2022). A Fast and Interpretable Deep Learning Approach for Accurate Electrostatics-Driven pKa Predictions in Proteins. Journal of Chemical Theory and Computation, 18, 5068-5078. ISSN 1549-9618. eISSN 1549-9626. http://dx.doi.org/10.1021/acs.jctc.2c00308

IEEE
Pedro B.P.S. Reis, Marco Bertolini, Floriane Montanari, Walter Rocchia, Miguel Machuqueiro, Djork-Arné Clevert, "A Fast and Interpretable Deep Learning Approach for Accurate Electrostatics-Driven pKa Predictions in Proteins" in Journal of Chemical Theory and Computation, vol. 18, pp. 5068-5078, 2022. 10.1021/acs.jctc.2c00308

BIBTEX
@article{57036, author = {Pedro B.P.S. Reis and Marco Bertolini and Floriane Montanari and Walter Rocchia and Miguel Machuqueiro and Djork-Arné Clevert}, title = {A Fast and Interpretable Deep Learning Approach for Accurate Electrostatics-Driven pKa Predictions in Proteins}, journal = {Journal of Chemical Theory and Computation}, year = 2022, pages = {5068-5078}, volume = 18 }