RESUMO

Background: The impact of schistosomiasis, which affects over 230 million people, emphasizes the urgency of developing new antischistosomal drugs. Artificial intelligence is vital in accelerating the drug discovery process. Methodology & results: We developed classification and regression machine learning models to predict the schistosomicidal activity of compounds not experimentally tested. The prioritized compounds were tested on schistosomula and adult stages of Schistosoma mansoni. Four compounds demonstrated significant activity against schistosomula, with 50% effective concentration values ranging from 9.8 to 32.5 µM, while exhibiting no toxicity in animal and human cell lines. Conclusion: These findings represent a significant step forward in the discovery of antischistosomal drugs. Further optimization of these active compounds can pave the way for their progression into preclinical studies.

Assuntos

Esquistossomose , Esquistossomicidas , Animais , Humanos , Schistosoma mansoni , Inteligência Artificial , Esquistossomicidas/farmacologia , Esquistossomose/tratamento farmacológico , Descoberta de Drogas

RESUMO

The discovery of the importance of kinase activity and its relationship to the emergence and proliferation of cancer cells, due to changes in normal physiology, opened a remarkable pathway for the treatment of chronic myelogenous leukemia through intense search of drug candidates. Six Abl kinase inhibitors have received the US FDA approval as chronic myelogenous leukemia treatment, and continuous efforts in obtaining new, more effective and selective molecules are being carried out. Herein we discuss the mechanisms of Abl inhibition, structural features and ligand/protein interactions that are important for the design of new Abl kinase inhibitors. This review provides a broad overview of binding mode predictions, through molecular docking, which can be an approach to discover novel Abl kinase inhibitors.