JBCS

1:25, Mon Mar 31

Open Access/APCs

First slide
Second slide
Third slide
Third slide

Recent Papers - Machine Learning Special Issue




Full Paper J. Braz. Chem. Soc. 2025

Assessing Emissions of Biogenic Volatile Organic Compounds and Their Correlation with Abiotic Factors in an Atlantic Forest Reserve Using Supervised Learning Methods

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.

https://dx.doi.org/10.21577/0103-5053.20250050

Published online: March 25, 2025.

 

PDF

Total access: 84

  Supplementary Information 

Total access: 84


J. Braz. Chem. Soc. 2025

Machine Learning to Treat Data for the Design and Improvement of Electrochemical Sensors: Application for a Cancer Biomarker

Gisela Ibáñez Redín; Daniel C. Braz; Débora Gonçalves; Osvaldo N. Oliveira Jr.

Full voltammogram analysis through machine learning for enhanced detection in electrochemical immunosensors.

https://dx.doi.org/10.21577/0103-5053.20250043

Published online: March 14, 2025.

 

PDF

Total access: 104

  Supplementary Information 

Total access: 104


J. Braz. Chem. Soc. 2025

Machine Learning Prediction of the Most Intense Peak of the Absorption Spectra of Organic Molecules

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.

https://dx.doi.org/10.21577/0103-5053.20250037

Published online: March 7, 2025.

 

PDF

Total access: 154

  Supplementary Information 

Total access: 154


J. Braz. Chem. Soc. 2025

Computational Modeling and Biological Evaluation of Benzophenone Derivatives as Antileishmanial Agents

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.

https://dx.doi.org/10.21577/0103-5053.20250004

Published online: February 14, 2025.

 

PDF

Total access: 264

  Supplementary Information 

Total access: 264


J. Braz. Chem. Soc. 2025

Discrimination between COVID-19 Positive and Negative Blood Sera Using an Unmodified Disposable Impedimetric Sensor and Multivariate Analysis

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.

https://dx.doi.org/10.21577/0103-5053.20250029

Published online: February 14, 2025.

 

PDF

Total access: 251

  Supplementary Information 

Total access: 251


J. Braz. Chem. Soc. 2025

Drug Repurposing for Trypanosomiasis: Using Machine Learning Models and Polypharmacology to Identify Multitarget Candidates

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.

https://dx.doi.org/10.21577/0103-5053.20250028

Published online: February 13, 2025.

 

PDF

Total access: 257

  Supplementary Information 

Total access: 257


J. Braz. Chem. Soc. 2025

Deep Reinforcement Learning and Structure-Based Approaches in the de novo Design of a New Potential Inhibitor of F13 Protein from Monkeypox Virus

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.

https://dx.doi.org/10.21577/0103-5053.20250022

Published online: February 6, 2025.

 

PDF

Total access: 307

  Supplementary Information 

Total access: 307


J. Braz. Chem. Soc. 2025

Computational Investigations on Inhibitors of Mycobacterium tuberculosis Shikimate Kinase: Machine Learning, Docking, Molecular Dynamics and Free Energy Calculations

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.

https://dx.doi.org/10.21577/0103-5053.20250016

Published online: January 31, 2025.

 

PDF

Total access: 321

  Supplementary Information 

Total access: 321


Short Report J. Braz. Chem. Soc. 2024

Effect of the Alkyl Side Chain of Antitrypanosomal Cinnamate, p-Coumarate, and Ferulate n-Alkyl Esters Using Multivariate Analysis and Computer-Aided Drug Design

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.

https://dx.doi.org/10.21577/0103-5053.20240203

Published online: October 28, 2024.

 

PDF

Total access: 665

  Supplementary Information 

Total 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

GN1