Background: The development of Multi-Target-Directed Ligands (MTDLs) has emerged as a significant strategy for addressing complex, overlapping pathologies such as cancer and Alzheimer’s disease (AD). This study aims to provide a robust computational framework for the design of dual-target inhibitors. Methods: This study presents an integrated and rigorous computational pipeline combining Quantitative Structure–Activity Relationship (QSAR) modeling, Molecular Docking, and Molecular Dynamics (MD) simulations with Dynamic Cross-Correlation Matrix (DCCM) analysis. Using a dataset of 57 known tubulin inhibitors, two high-performing QSAR models were developed to guide the rational design of 16 novel trimethoxyphenyl-based analogues. Results: Following ADMET and drug-likeness filtering, Lead Candidates 15 and 16 were identified. Quantitative activity predictions confirmed their enhanced potency thresholds, which were subsequently validated through static docking against β-tubulin (PDB: 4O2B) and Acetylcholinesterase (PDB: 1EVE). In total, 100 ns MD simulations and MM-GBSA calculations demonstrated superior binding stability and energetically favorable profiles for both targets, while DCCM analysis confirmed the functional synchrony of the protein–ligand complexes. Conclusions: The results provide a validated structural hypothesis for dual-target inhibition. The identified leads, 15 and 16, demonstrate strong predictive potential and are prioritized for chemical synthesis and in vitro biological evaluation.

Alzheimer’s disease (AD) represents an escalating global neuropharmacological crisis, with prevalence in high-growth demographic regions such as India projected to exceed 14 million by 2040. This study addresses the urgent need for high-potency, dual-site acetylcholinesterase (AChE) inhibitors through an integrated computational pipeline. We address the failure of mono-target paradigms by designing scaffolds capable of simultaneously anchoring the Catalytic Active Site (CAS) and the Peripheral Anionic Site (PAS). A robust GA-MLR QSAR model was developed from 115 quinoline analogs using 11,135 descriptors. Lead candidates were prioritized via cavity directed molecular docking (7XN1) and 100 ns molecular dynamics (MD) simulations. The five-descriptor model (R² = 0.7569, $Q_{LOO}^2$ = 0.7244) was validated by an external set of 8 experimental compounds ($R_{ext}^2$ = 0.8620). Lead Compound 19 emerged as a superior candidate (ΔG = −11.1 kcal/mol), exhibiting a stable MD trajectory (PL-RMSD ≈ 2.4 Å) and preserving essential Gly121-His447 catalytic anti-correlations. This study provides a statistically validated scaffold and computational mechanistic foundation for future in vitro experimental validation, advancing the high throughput screening of neuroprotective agents on a global scale.

Aim: To evaluate the Neuroprotective activity of Biophytum reinwardtii Platinum nanoparticles Methods: Biophytum reinwerdtii platinum nanoparticles were subjected to evaluation of the neuroprotection activity in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine induced zebra fishes. Experimental fishes are divided into 5 groups, each containing 8 fishes. Group I is considered a normal group; Group II is a toxic group means treated with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine 225 mg/kg, i.p. for 5 days; Group III, IV, and V are treatment groups means treated with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (225 mg/kg) with 0.3 µmol, 0.4µmol, and 0.5 µmol of Biophytum reinwardtii Platinum nanoparticles respectively for 5 days. Results: In the in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine group, locomotor activity and complex I activity are decreased, Malondialdehyde levels increased, antioxidants, and catecholamines levels decreased, whereas Biophytum reinwardtii Platinum nanoparticles treated fishes exhibit significant locomotor and increased levels of antioxidants and catecholamines. Conclusion: These results suggest that Biophytum reinwardtii Platinum nanoparticles. Showed significant neuroprotection activity in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine -induced Parkinson’s zebra fishes.

The research examines how Artificial Intelligence (AI) can be applied to revolutionize the development of analytical methods by leveraging the weaknesses of the traditional trial-and-error method. Based on the descriptive and exploratory research design, the study is a synthesis of secondary sources in 50 peer-reviewed articles, industry reports, and case-based studies that were published between 2015 and 2025. The results indicate that AI-supported approaches are far much better than traditional ones in a variety of aspects. The optimization cycles were cut by 58 percent, parameter accuracy also improved by 19 percent, and the general efficiency of the process was enhanced by a cut in the development time of 45 percent, a reduction in cost of 38 percent and reduction in human error by 52 percent. Pharmaceutical analysis, environmental chemistry, and food quality testing case studies further attested that the reproducibility always increased above 89 percent in contrast with a common of 73 percent through the conventional techniques. In spite of these developments, problems like the scarcity of data, interpretation problems, and regulatory acceptability were discovered. Nevertheless, potential adoption in the future is high where it is estimated that the integration level is more than 77 percent in all of the categories evaluated. The paper highlights the role of AI in improving efficiency, accuracy and reproducibility and minimizing dependency on resources hence presenting substantial advantages to the laboratories, industries and regulatory authorities. On the whole, AI can be discussed as one of the defining tools that can make an analytical procedure redefined, innovative, and become more reliable in terms of scientific practices.

This chapter explores the transformation of education through technology, focusing on the shift from traditional to digital methods. Key innovations like artificial intelligence, virtual and augmented reality, learning management systems, and cloud computing have revolutionized personalized learning and accessibility. Historical developments, from early educational tools to MOOCs, highlight their role in expanding global reach. The chapter addresses challenges such as the digital divide, quality assurance, and teacher re-skilling, emphasizing the importance of policies in fostering inclusive education. Future directions include hybrid models, immersive learning experiences, and AI-driven systems, blending traditional and digital approaches to create dynamic, student-centered environments.

Analytical method validation (AMV) in the veterinary field forms an important aspect of quality control in pharmaceutical companies in which drugs are used to treat various species of animals to guarantees safety, efficacy and regulatory actions are adhered to. This review points to the significance of the species-based approaches in pharmacokinetics of drug absorption, distribution, metabolism, and residue detection with a focus on the issues of physiological diversity, complex biological samples and ethical requirement. Modern methods such as HPLC, UPLC, GC, LC-MS/MS, UV-Vis, and FTIR are characterized by sensitivity, specificity, and reproducibility and newer techniques such as capillary electrophoresis and microfluidic-based technology, high-resolution mass spectrometry and bioinformatics-directed validation incorporates enhanced sensitivity, specificity, reproducibility, reduced sample volumes, and increased throughput. Future methodological advances in multi-dimensional platforms, green analytical chemistry, chemometrics, and machine learning offer new ways of addressing matrix effects and resource limitations and regulatory limitations. Taken together, these strategies can contribute to effective veterinary drug monitoring, food safety, and sustainable and species-specific AMV practices and direct future research at achieving harmonized and technologically more advanced validation processes.