Recent Papers - Machine Learning Special Issue
Reviews/Accounts - last 5 years
This work paper the applications of Natural Language Processing (NLP) and Large Language Models (LLMs) in chemistry and materials science, highlighting their role in chemical entity recognition, reaction prediction, materials discovery, and literature analysis.
Igor H. Sanches; Francisco L. Feitosa; Jade M. Lemos; Sabrina Silva-Mendonça; Ester Souza; Victoria F. Cabral; José T. Moreira-Filho; Henric Gil; Bruno J. Neves; Rodolpho C. Braga; Joyce V. V. B. Borba; Carolina H. Andrade
The figure illustrates the core components of quantitative structure-activity relationship (QSAR)-Lit, a platform designed to streamline the QSAR modeling process. It encompasses data curation, descriptor calculation, machine learning, and virtual screening, enabling seamless and efficient analysis for drug discovery applications.
Maicon Pierre Lourenço ; Mosayeb Naseri; Lizandra Barrios Herrera; Hadi Zadeh-Haghighi
; Daya Gaur; Christoph Simon;
Dennis R. Salahub
A quantum active learning method (QAL) for automatic structural determination of doped materials has been developed and implemented in the QMLMaterial software. QAL uses quantum circuits for data encoding to create quantum machine learning models on-the-fly.
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: 323
Supplementary InformationTotal access: 323
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: 314
Supplementary InformationTotal access: 314
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: 330
Supplementary InformationTotal access: 330
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: 370
Supplementary InformationTotal access: 370
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: 384
Supplementary InformationTotal access: 384
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: 715
Supplementary InformationTotal access: 715
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