EVENTO
Prediction of antimicrobial combinations activity through the use of chemBERTa pre-trained model representation
Tipo de evento: Defesa de Dissertação de Mestrado
The combination of two or more drugs is a promising strategy to combat the increasing Antimicrobial Resistance (AMR) crisis. Synergistic combinations are particularly valuable, as they potentially reduce the mutation rate of resistance development. However, the high cost required for experimental screening is challenging, and, up to date, only one research group has made a significant advance. Building upon their study, we developed a two-step Machine Learning approach for the in silico prediction of drug combinations effects.We first represent drugs as embeddings using the pre-trained chemBERTa model, leveraging transfer learning. In the first classification step, a model distinguishes between additive and non-additive effects. To address class imbalance, we develop a custom undersampling method that maintains the chemical diversity of the dataset. A second model then classifies non-additive effects as either antagonistic or synergistic across six bacterial strains.The results show that the LightGBM model consistently outperformed other models in both classification tasks. The addictive vs non-additive models demonstrate a high performance (AU-ROC> 0.8), identifying the most common additive effects and providing a potential tool for prioritizing promising drug pairs. Notably, models trained on resistant strains consistently achieved higher performance than those trained on susceptible strains, suggesting that the non-additive effect in resistant bacteria may be restricted to a more predictable space of combinations. While the antagonism vs. synergism models require further refinement, they hold significant potential for predicting synergistic effects.Evento HíbridoLocal: Auditório BLink de transmissão:meet.google.com/stp-ybqp-srj
Data Início: 09/09/2025 Hora: 14:00 Data Fim: 09/09/2025 Hora: 17:00
Local: LNCC - Laboratório Nacional de Computação Ciêntifica - Auditorio B
Aluno: Alex Fabricio Sánchez Yumbo - - LNCC
Orientador: Isabella Alvim Guedes - Laboratório Nacional de Computação Científica - LNCC Laurent Emmanuel Dardenne - Laboratório Nacional de Computação Científica - LNCC Marisa Fabiana Nicolás - Laboratório Nacional de Computação Científica - LNCC
Participante Banca Examinadora: Carlos Alberto Brizuela Rodríguez - Ensenada Center for Scientific Research and Higher Education - CICESE Marisa Fabiana Nicolás - Laboratório Nacional de Computação Científica - LNCC Rômulo Gonçalves Agostinho Galvani - - LNCC
Suplente Banca Examinadora: Eduardo Krempser da Silva - Fundação Oswaldo Cruz - FIOCRUZ Fabio Lima Custodio - Laboratório Nacional de Computação Científica - LNCC
