In the present study, the phytochemical study of the n-hexane extract from flowers of Nectandra leucantha (Lauraceae) afforded six known neolignans (1–6) as well as one new metabolite (7), which were characterized by analysis of NMR, IR, UV, and ESI-HRMS data. The new compound 7 exhibited potent activity against the clinically relevant intracellular forms of T. cruzi (amastigotes), with an IC₅₀ value of 4.3 μM and no observed mammalian cytotoxicity in fibroblasts (CC₅₀ > 200 μM). Based on the results obtained and our previous antitrypanosomal data of 50 natural and semi-synthetic related neolignans, 2D and 3D molecular modeling techniques were employed to help the design of new neolignan-based compounds with higher activity. The results obtained from the models were important to understand the main structural features related to the biological response of the neolignans and to aid in the design of new neolignan-based compounds with better biological activity. Therefore, the results acquired from phytochemical, biological, and in silico studies showed that the integration of experimental and computational techniques consists of a powerful tool for the discovery of new prototypes for development of new drugs to treat CD.

Background: Natural products have been universally approached in the research of novel trends useful to detail the essential paths of the life sciences and as a strategy for pharmacotherapeutics. Objective: This work focuses on further modification to the 6-hydroxy-flavanone building block aiming to obtain improved BCR-ABL kinase inhibitors. Methods: Ether derivatives were obtained from Williamson synthesis and triazole from Microwave- assisted click reaction. Chemical structures were finely characterized through IR, 1H and 13C NMR and HRMS. They were tested for their inhibitory activity against BCR-ABL kinase. Results: Two inhibitors bearing a triazole ring as a pharmacophoric bridge demonstrated the strongest kinase inhibition at IC50 value of 364 nM (compound 3j) and 275 nM (compound 3k). Conclusion: 6-hydroxy-flavanone skeleton can be considered as a promising core for BCR-ABL kinase inhibitors.

Aim: Type 2 diabetes mellitus and metabolic syndrome are two diseases related to disorders of lipid and carbohydrate metabolism and insulin resistance. Peroxisome proliferator-activated receptors (PPARs) are a class of nuclear receptors that control the metabolism of lipids/carbohydrates and are considered targets for both diseases. PPAR affinity and selectivity are critical points to design drug candidates with appropriated pharmacodynamic/kinetic profiles. Materials & methods: Hologram quantitative structure–activity relationships studies were conducted, as well molecular docking and molecular interaction field calculations, in order to explain affinity and selectivity of selected compounds. Results: The constructed hologram quantitative structure–activity relationship models are robust and predictive (values of q2 and r2 test above 0.70). Conclusion: The quantitative structure–activity relationship models and docking/GRID analyses indicated that carboxyl group of indole-sulfonamide derivatives could in...

Este livro compila os artigos publicados na Revista BIOINFO Vol. 5 (2025). Todos os direitos autorais pertencem aos autores de cada respectivo artigo. Todavia, os autores concordam em compartilhar gratuitamente o conteúdo deste livro e incentivam sua livre distribuição (desde que os autores dos respectivos capítulos sejam corretamente citados e/ou que as leis de uso justo sejam respeitadas). Este livro está compartilhado sob a licença Creative Commons Atribuição 4.0 Internacional - Não comercial (CC BY-NC 4.0). Você pode utilizar qualquer conteúdo aqui apresentado, desde que cite: Moura Junior, Adenilson Arcanjo de (org.) et al. BIOINFO #05 - Revista Brasileira de Bioinformática e Biologia Computacional. 5. Ed. Vol. 5. ISBN: 978-65-84391-01-7. Lagoa Santa: Alfahelix, 2025. doi: 10.51780/978-65-84391-01-7

Advancements in computational methods have significantly transformed the traditional drug discovery process, which has been historically lengthy and costly, often taking over a decade and billions of dollars. Virtual screening (VS) is a computational technique used in drug discovery to identify potential drug candidates by screening large chemical libraries. This technique has transformed drug discovery by enabling rapid and cost-effective screening of chemical libraries, reducing time and resources. This leverages algorithms, such as similarity-based, quantitative, and machine learning, to prioritize compounds binding to target proteins. Additionally, databases provide vast compound collections for screening. However, experimental validation is essential to ensure accuracy. In this chapter, the authors delve into the concepts and methodologies of this technique, focusing on its crucial role in drug discovery.

As doenças negligenciadas causadas por cinetoplastídeos, como a Doença de Chagas, afetam milhões de pessoas, e os tratamentos atuais são limitados por toxicidade e baixa eficácia. A busca por novos alvos terapêuticos é, portanto, urgente. A Proteína Ribossomal Específica de Cinetoplastídeos (KSRP) surge como um alvo promissor por ser exclusiva do parasito e essencial para sua sobrevivência. Contudo, suas vulnerabilidades estruturais permanecem pouco exploradas. Para solucionar este problema, este trabalho propõe uma metodologia computacional integrada para identificar e caracterizar sítios de interação na KSRP, visando guiar o desenvolvimento racional de futuros inibidores. A estratégia combina dados estruturais com ferramentas de bioinformática, como docking molecular e análise de flexibilidade, para mapear regiões funcionalmente relevantes. Este estudo fornece hipóteses estruturais para direcionar futuros ensaios de validação.