Clorhexidina como alternativa para maximizar la longevidad de restauraciones adhesivas

La resistencia de la interfase de unión dentina-resina puede verse directamente afectada al mediano y largo plazo debido a alteraciones morfológicas que revelan la desnaturalización parcial o completa de los constituyentes de esta interfase. Algunos protocolos vienen siendo estudiados con el fin de preservar esta interfase a lo largo del tiempo. Este artículo presenta el reporte de un caso clínico donde fue utilizada clorehexidina, como parte del procedimiento adhesivo, con el objetivo aumentar la longevidad de una restauración con resina compuesta. La revisión de literatura descrita justifica la técnica utilizada y destaca los beneficios de su uso

Artificial intelligence systems for the design of magic shotgun drugs

Designing magic shotgun compounds, i.e., compounds hitting multiple targets using artificial intelligence (AI) systems based on machine learning (ML) and deep learning (DL) approaches, has a huge potential to revolutionize drug discovery. Such intelligent systems enable computers to create new chemical structures and predict their multi-target properties at a low cost and in a time-efficient manner. Most examples of AI applied to drug discovery are single-target oriented and there is still a lack of concise information regarding the application of this technology for the discovery of multi-target drugs or drugs with broad-spectrum action. In this review, we focus on current developments in AI systems for the next generation of automated design of multi-target drugs. We discuss how classical ML methods, cutting-edge generative models, and multi-task deep neural networks can help de novo design and hit-to-lead optimization of multi-target drugs. Moreover, we present state-of-the-art workflows and highlight some studies demonstrating encouraging experimental results, which pave the way for de novo drug design and multi-target drug discovery