A Fast and Interpretable Deep Learning Approach for Accurate Electrostatics-Driven p<i>K</i><sub>a</sub> Predictions in Proteins-Reference-Cited by-同舟云学术

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

Author:

Reis Pedro B.P.S.¹^ORCID,Bertolini Marco¹,Montanari Floriane¹^ORCID,Rocchia Walter²^ORCID,Machuqueiro Miguel³^ORCID,Clevert Djork-Arné¹

Affiliation:

1. Machine Learning Research, Bayer A.G., Berlin 13353, Germany

2. CONCEPT Lab, Istituto Italiano di Tecnologia (IIT), Via Melen 83, B Block, Genoa 16152, Italy

3. Biosystems and Integrative Sciences Institute (BioISI), Faculty of Sciences, University of Lisboa, Campo Grande, Lisboa 1749-016, Portugal

Funder

Bayer

H2020 Research Infrastructures

Funda??o para a Ci?ncia e a Tecnologia

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

Reference49 articles.

Cited by 11 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

4. Open-Source Machine Learning in Computational Chemistry;Journal of Chemical Information and Modeling;2023-07-19