RESUMEN
The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.
RESUMEN
The existence of methylene blue (MB) in wastewater even as traces is raising environmental concerns. In this regard, the performances of four adsorbents, avocado stone biochar (AVS-BC), montmorillonite (MMT), and their magnetite Fe3O4-derived counterparts, were compared. Results showed the superior performance of Fe3O4@AVS-BC and Fe3O4@MMT nanocomposites with removal percentages (%R) of 95.59% and 88%. The morphological features of AVS-BC as revealed by SEM analysis showed a highly porous surface compared to a plane and smooth surface in the case of MMT. Surface analysis using FT-IR and Raman spectroscopies corroborated the existence of the Fe-O peaks upon loading with magnetite. The XRD analysis confirmed the formation of cubic magnetite nanoparticles. The adsorption process in the batch mode was optimized using central composite design (CCD). Equilibrium and kinetic isotherms showed that the adsorption of MB onto Fe3O4@AVS-BC fitted well with the Langmuir isotherm and the pseudo-second-order (PSO) model. The maximum adsorption capacity (qm) was 118.9 mg/g (Fe3O4@AVS-BC) and 72.39 mg/g (Fe3O4@MMT). The Fe3O4@AVS-BC showed a higher selectivity toward MB compared to other organic contaminants. The MB-laden adsorbent was successfully used for the remediation of Cr (III), Ni (II), and Cd (II) with removal efficiencies hitting 100% following thermal activation.
