Enzyme–substrate interactions play a fundamental role in elucidating synthesis pathways and synthetic biology, as they allow for the understanding of important aspects of a reaction. Establishing the interaction experimentally is a slow and costly process, which is why this problem has been addressed using computational methods such as molecular dynamics, molecular docking, and Monte Carlo simulations. Nevertheless, this type of method tends to be computationally slow when dealing with a large search space. Therefore, in recent years, methods based on artificial intelligence, such as support vector machines, neural networks, or decision trees, have been implemented, significantly reducing the computing time and covering vast search spaces. These methods significantly reduce the computation time and cover broad search spaces, rapidly reducing the number of interacting candidates, as they allow repetitive processes to be automated and patterns to be extracted, are adaptable, and have the capacity to handle large amounts of data. This article analyzes these artificial intelligence-based approaches, presenting their common structure, advantages, disadvantages, limitations, challenges, and future perspectives.

Background/Objectives: The rational design of modified-release matrix tablets requires a thorough understanding of granulometric analysis, compaction behavior, and drug release profile. In this study, we evaluated the physicochemical, granulometric, and mechanical properties of hydroxypropyl methylcellulose, polyethylene oxide, and ethylcellulose in galantamine matrix formulations. Methods: Spectroscopic (FTIR) and thermal (DSC) analyses demonstrated drug–polymer compatibility. We assessed flowability, cohesion, and aeration behavior through granulometric analysis and applied compressibility models (Kawakita, Heckel, Leuenberger) to characterize deformation mechanisms. Results: Hydroxypropyl methylcellulose showed superior compactability (T_max = 4.61 MPa) and sustained drug release (85.4% at 12 h, DE% = 62.2%), while polyethylene oxide enabled gradual erosion and consistent delivery (88.7% at 12 h, DE% = 57.5%). In contrast, ethylcellulose exhibited high cohesiveness but poor matrix integrity, leading to premature drug release (76.6% at 1 h, DE% = 73.7%). Only hydroxypropyl methylcellulose and polyethylene oxide formulations met USP criteria. Conclusions: These results demonstrate that polymer selection critically influences powder behavior and matrix performance, underscoring the need for integrated granulometric and mechanical evaluation in the development of robust controlled-release systems.

Dengue (DENV) and Zika (ZIKV) virus continue to pose significant challenges globally due to their widespread prevalence and severe health implications. Given the absence of effective vaccines and specific therapeutics, targeting the highly conserved NS5 RNA-dependent RNA polymerase (RdRp) domain has emerged as a promising strategy. However, limited efforts have been made to develop inhibitors for this crucial target. In this study, we employed an integrated in silico approach utilizing combinatorial chemistry, docking, molecular dynamics simulations, MM/GBSA, and ADMET studies to target the allosteric N-pocket of DENV3-RdRp and ZIKV-RdRp. Using this methodology, we designed lycorine analogs with natural S-enantiomers (LYCS) and R-enantiomers (LYCR) as potential inhibitors of non-structural protein 5 (NS5) in DENV3 and ZIKV. Notably, 12 lycorine analogs displayed a robust binding free energy (<−9.00 kcal/mol), surpassing that of RdRp-ribavirin (<−7.00 kcal/mol) along with promising ADMET score predictions (<4.00), of which (LYCR728-210, LYCS728-210, LYCR728-212, LYCS505-214) displayed binding properties to both DENV3 and ZIKV targets. Our research highlights the potential of non-nucleoside lycorine-based analogs with different enantiomers that may present different or even completely opposite metabolic, toxicological, and pharmacological profiles as promising candidates for inhibiting NS5-RdRp in ZIKV and DENV3, paving the way for further exploration for the development of effective antiviral agents.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus has dramatically impacted human health. It continues to be a threat to modern society because many people die as a result of infection. The disease is diagnosed using serologic and molecular tests, such as the gold standard real-time polymerase chain reaction (RT-PCR). The last has several disadvantages because it requires specialized infrastructure, costly equipment, and trained personnel. Here, we present a protocol outlining the steps required to detect the SARS-CoV-2 virus using reverse transcription-loop-mediated isothermal amplification (RT-LAMP) in human samples. The protocol includes instructions for designing primers in silico, preparing reagents, amplification, and visualization. Once standardized, this method can be easily implemented and adapted to any laboratory or point-of-care within 60 min at a low cost and using inexpensive equipment. It is adaptable to detecting different pathogens. Thus, it can potentially be used in the field and in health centers to carry out timely epidemiological surveillance.

Dengue (DENV) and Zika (ZIKV) virus continue to pose significant challenges globally due to their widespread prevalence and severe health implications. Given the absence of effective vaccines and specific therapeutics, targeting of the highly conserved NS5 RNA-dependent RNA polymerase (RdRp) domain has emerged as a promising strategy. However, limited efforts have been made to develop inhibitors for this crucial target. In this study, we employed an integrated in silico approach utilizing combinatorial chemistry, docking, molecular dynamics simulations, MM/GBSA, and ADMET studies to target the allosteric N-pocket of DENV3-RdRp and ZIKV-RdRp. Using this methodology, we designed lycorine analogs with natural S-enantiomers (LYCS) and R-enantiomers (LYCR) as potential inhibitors of non-structural protein 5 (NS5) in DENV3 and ZIKV. Notably, 12 lycorine analogs displayed a robust binding free energy (<-9.00 kcal/mol), surpassing that of RdRp-Ribavirin (<-7.00 kcal/mol) along with promising ADMET score predictions (<4.00), of which (LYCR728-210, LYCS728-210, LYCR728-212, LYCS505-214) displayed binding properties to both DENV3 and ZIKV targets. This study highlights the potential of non-nucleoside lycorine-based analogs with different enantiomers that may present different or even completely opposite metabolic, toxicological, and pharmacological profiles as promising candidates for inhibiting NS5-RdRp in ZIKV and DENV3, paving the way for further exploration for the development of effective antiviral agents.

Alkaloids are among the most important secondary metabolites, and the discovery of their biosynthetic pathways is a key focus for pharmacological applications. Nowadays, these discoveries rely heavily on data from genomes, transcriptomes, and metabolomes. Since the 2000s, RNA sequencing has become a common approach for uncovering metabolic pathways by correlating plant transcript expression with metabolomic profiling. While the biosynthesis routes for isoquinoline alkaloids (IsAs) are well-documented in the literature, those for Amaryllidaceae alkaloids (AmAs) remain poorly described. In this review, we compile the available information on the genomic and transcriptomic approaches used in plants producing IsAs and AmAs and explore how the similarities between these two groups contribute to understanding the current challenges in elucidating AmAs pathways. Additionally, we compare historical approaches to discovering IsAs and AmAs pathways by analyzing key representatives—lycorine and galanthamine (AmAs) and morphine (IsAs)—while highlighting the role of various enzyme families involved in the synthesis of these alkaloids.