APRENDIZADO DE MÁQUINA APLICADO A QSAR

• Published in: Química Nova Vol. 47; no. 7
• Main Authors: de Menezes, Renata, Scotti, Luciana, Scotti, Marcus
• Format: Journal Article
• Language: English
• Published: Sociedade Brasileira de Química 2024
• ISSN: 0100-4042; 1678-7064; 1678-7064
• DOI: 10.21577/0100-4042.20240024

MACHINE LEARNING APLLIED TO QSAR. Over the years the study of the quantitative structure-activity relationship (QSAR) has transformed from a simple regression analysis to the implementation of machine learning (ML) with multiple statistics. Today ML-based QSAR models are quite important and play a notable role in drug design and screening, property prediction, biological activity, etc. ML methods applied to QSAR build classification or regression models to describe/predict the complex relationships between the chemical structure of molecules and biological activity. Even with the increase in scientific publications addressing this topic written in Portuguese, there is still a shortage of scientific articles explaining ML techniques applied to QSAR, how to build models, the types of models, algorithms, for the Brazilian scientific community. And to fill this need, we intend to approach the subject in a simple and didactic way for students and researchers who are starting in this very promising and important area. We will describe the fully explained theory of machine learning by applying QSAR, abstracting the complexity, and well-illustrated.