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Contribution of a special issue on Antarctic research to the AABC impact factor.
An Acad Bras Cienc
December 2024
Universidade Federal do Rio de Janeiro, Museu Nacional, Laboratório de Sistemática e Tafonomia de Vertebrados Fósseis, Departamento de Geologia e Paleontologia, Quinta da Boa Vista, s/n, São Cristóvão, 20940-040 Rio de Janeiro, RJ, Brazil.
Contribution of the special issue on the BRICS Astronomy Working Group to the AABC impact factor.
An Acad Bras Cienc
December 2024
Universidade Federal do Rio de Janeiro, Museu Nacional, Laboratório de Sistemática e Tafonomia de Vertebrados Fósseis, Departamento de Geologia e Paleontologia, Quinta da Boa Vista, s/n, São Cristóvão, 20940-040 Rio de Janeiro, RJ, Brazil.
A new issue of the AABC focused on Antarctic research.
An Acad Bras Cienc
November 2023
Universidade Federal do Rio de Janeiro, Museu Nacional, Laboratório de Sistemática e Tafonomia de Vertebrados Fósseis, Departamento de Geologia e Paleontologia, Quinta da Boa Vista, s/n, São Cristóvão, 20940-040 Rio de Janeiro, RJ, Brazil.
Modeling and Estimation of CO Emissions in China Based on Artificial Intelligence.
Comput Intell Neurosci
July 2022
Engineering Research Center for Seismic Disaster Prevention and Engineering Geological Disaster Detection of Jiangxi Province (East China University of Technology), Nanchang, Jiangxi 330013, China.
Since China's reform and opening up, the social economy has achieved rapid development, followed by a sharp increase in carbon dioxide (CO) emissions. Therefore, at the 75th United Nations General Assembly, China proposed to achieve carbon peaking by 2030 and carbon neutrality by 2060. The research work on advance forecasting of CO emissions is essential to achieve the above-mentioned carbon peaking and carbon neutrality goals in China.
View Article and Find Full Text PDFDeveloping a Kinase-Specific Target Selection Method Using a Structure-Based Machine Learning Approach.
Adv Appl Bioinform Chem
December 2020
Bioinformatics Project, National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan.
Introduction: Despite recent advances in the drug discovery field, developing selective kinase inhibitors remains a complicated issue for a number of reasons, one of which is that there are striking structural similarities in the ATP-binding pockets of kinases.
Objective: To address this problem, we have designed a machine learning model utilizing various structure-based and energy-based descriptors to better characterize protein-ligand interactions.
Methods: In this work, we use a dataset of 104 human kinases with available PDB structures and experimental activity data against 1202 small-molecule compounds from the PubChem BioAssay dataset "Navigating the Kinome".
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