Recent Papers - Machine Learning Special Issue
Reviews/Accounts - last 5 years
Ana Paula S. Figueiredo; Junio R. Botelho; Marcia Helena C. Nascimento ; Maria Cristina Canela
; Royston Goodacre;
Paulo R. Filgueiras; Murilo O. Souza
This study employs supervised learning methods, including Partial Least Squares Discriminant Analysis (PLS-DA) with bootstrap resampling and Support Vector Machine (SVM) ensemble, to analyze biogenic volatile organic compounds (BVOCs) emissions in the Atlantic Forest, achieving high classification accuracy.
Gisela Ibáñez Redín; Daniel C. Braz; Débora Gonçalves; Osvaldo N. Oliveira Jr.
Full voltammogram analysis through machine learning for enhanced detection in electrochemical immunosensors.
Rubens C. Souza ; Julio C. Duarte
; Ronaldo R. Goldschmidt
; Itamar Borges Jr.
Molecules from the QM-symex database are converted to SMILES (simplified molecular input line entry system) and stored in a new QM-symex-modif dataset with their target properties. The data is processed and used in machine learning models to develop predictive models for photophysical properties.
Bárbara F. Farias ; Miller S. Ferreira
; Daniel O. Miranda
; Tayná R. Nunes; Natália F. Pereira; Patrícia F. Espuri
;
Jaqueline P. Januario ; Fábio A. Colombo
; Marcos J. Marques
; João L. B. Zanin
; Marisi G. Soares
; Thiago B. de Souza
; Diogo T. Carvalho
; Daniela A. Chagas-Paula
; Danielle F. Dias
Application of machine learning and computational tools to predict antileishmanial activity, emphasizing the synergy of computational and experimental methods in developing novel therapeutic agents.
Total access: 264
Supplementary InformationTotal access: 264
Ingrid G. B. L. Cruz; Flávia R. P. Sales; Wallace D. Fragoso ; Lúcio R. C. Castellano; Fabyan E. L. Beltrão; Talita N. Cardoso;
Maísa S. de Oliveira; Sherlan G. Lemos
The blood composition imbalance following coronavirus disease (COVID-19) causes a systematic change in impedance, which can be modelled by multivariate analysis.
Total access: 251
Supplementary InformationTotal access: 251
Karime Zeraik A. Domingues; Alexandre de F. Cobre; Mariana M. Fachi; Raul Edison L. Lazo; Luana M. Ferreira;
Roberto Pontarolo
This study utilizes Quantitative Structure-Activity Relationship (QSAR)-based machine learning models, validated with bioactivity data median inhibitory concentration (IC50) of compounds against Trypanosoma brucei and Trypanosoma cruzi, for screening Food and Drug Administration (FDA)-approved compounds as candidates for repurposing in the treatment of both trypanosomiases.
Total access: 257
Supplementary InformationTotal access: 257
Edilson B. Alencar Filho ; Rosalvo F. Oliveira Neto; Vanessa C. Santos; Allysson L. S. Ferreira
De novo design of a new lead compound with potential inhibitory effect on monkeypox virus F13 protein (VP37) by deep reinforcement learning and structure-based drug design.
Total access: 307
Supplementary InformationTotal access: 307
Anderson J. A. B. dos Santos; Paulo A. Netz
Machine learning combined with virtual screening has enabled the discovery of a significant variety of molecules exhibiting high affinity with shikimate kinase. Subsequent evaluation through molecular dynamics and free energy calculations has facilitated the identification of potential inhibitor candidates.
Total access: 321
Supplementary InformationTotal access: 321
Matheus L. Silva; João L. Baldim; Thais A. Costa-Silva; Maiara Amaral; Maiara M. Romanelli; Erica V. C. Levatti; Andre G. Tempone; João Henrique G. Lago
Machine learning and multivariate statistical analyses identified molecular features correlated with biological activity of phenylpropanoid against Trypanosoma cruzi.
Total access: 665
Supplementary InformationTotal access: 665
Online version ISSN 1678-4790 Printed version ISSN 0103-5053
Journal of the Brazilian Chemical Society
JBCS Editorial and Publishing Office
University of Campinas - UNICAMP
13083-970 Campinas-SP, Brazil
Free access