Phytochemicals from Curcuma longa and Panax ginseng were evaluated for their potential to modulate Interleukin-6 (IL-6), a cytokine implicated in myocarditis. Using the threedimensional structure of IL-6 (PDB ID: 4ZS7), a computational screening approach was applied to assess the binding interactions of 554 plant-derived compounds. Molecular docking simulations, performed with the Schrödinger Maestro Suite, revealed that Panax ginseng compounds exhibited strong binding affinity to IL-6, with docking scores of-9.251,-8.645, and-7.944 kcal/mol. Curcuma longa compounds showed moderate binding, with scores of-6.522,-6.336, and-5.985 kcal/mol. MM-GBSA calculations confirmed the stability of these interactions, particularly for Panax ginseng compounds, which displayed favourable binding energies of-54.92,-50.47, and-50.36 kcal/mol. Pharmacokinetic analysis indicated that the selected compounds fulfilled key drug-likeness criteria, including favourable oral absorption and solubility. These findings suggest that phytochemicals from Curcuma longa and Panax ginseng demonstrate strong potential as leads for future therapeutic development targeting IL-6 in inflammatory conditions such as myocarditis.

Monkeypox virus (MPXV), a zoonotic smallpox-related virus, has re-emerged as a global health issue, with increasing numbers of cases outside its traditional endemic regions. The virus contains a profilin-like protein, A42R, which, although with poor actin binding, participates in cytoskeletal rearrangements supporting viral replication and transmission. We utilized an insilico approach in examining the therapeutic potential of Aloe barbadensis and Allium sativum phytochemicals against target protein A42R in this study. We extracted 91 phytochemicals from the IMPPAT database and docked them using Schrödinger's Maestro Suite. The structure of the A42R protein (PDB ID: 4QWO) was optimized, and the binding site was predicted with the Sitemap module.14 compounds with high binding affinities were obtained through molecular docking using Glide's Standard Precision (SP) mode.From molecular docking with the standard precision (SP) mode of Glide, 14 compounds resulted with high binding affinities. The highest binding occurred with-7.23 kcal/mol from Allium sativum (protoeruboside B), followed by Aloe barbadensis (isoaloesin) at-5.29 kcal/mol. Furthermore, MM-GBSA calculations were performed for optimizations that resulted in a satisfactory range of binding free energies between-35 and-18 kcal/mol. All top compounds also passed drug likeness criteria, such as Lipinski's Rule of Five, by the ADME analysis carried out using QikProp. These results suggest phytochemicals from Allium sativum and Aloe barbadensis have promising antiviral activity against MPXV and warrant further investigation as potential lead compounds in the development of therapeutics targeting the A42R protein.

CRISPR-Cas systems have transformed genome engineering with their exceptional precision, programmability, and affordability. Although they originate from microbial defense mechanisms, expanding their use, especially in therapeutics, requires a chemically oriented framework that allows for tunable, reversible, and safe gene editing. This review offers a multidisciplinary look at recent progress in the structural, synthetic, and computational aspects of CRISPR-Cas technologies. Structural analyses examine the domain architectures of Cas enzymes, including the recognition (REC), nuclease (HNH and RuvC), and PAM-interacting domains, emphasizing the catalytic importance of divalent metal ions. Comparative insights into Cas9, Cas12, and Cas13 demonstrate functional diversity across DNA- and RNA-targeting systems, supported by high-resolution structural data on guide RNA pairing and conformational dynamics. The review highlights advances in chemical modulation, such as anti-CRISPR proteins, small-molecule inhibitors, and stimuli-responsive switches, focusing on structure–activity relationships. Additionally, bioorganic delivery systems like lipid nanoparticles, polymers, and cell-penetrating peptides are discussed for their role in improving in vivo delivery through formulation chemistry. Computational chemistry methods—molecular docking, molecular dynamics simulations, and virtual screening—are identified as critical tools for discovering and optimizing modulators. The use of AI-driven tools is proposed as a promising direction for rational CRISPR design. Overall, this chemistry-focused perspective emphasizes the importance of molecular control in developing the next generation of programmable and safe CRISPR-based therapies.

Computational and machine learning approaches play a pivotal role in identifying, characterizing, and targeting noncanonical DNA structures, including G-quadruplexes, Z-DNA, hairpins, and triplexes. These configurations play critical roles in maintaining genomic stability, facilitating DNA repair, and regulating chromatin organization. Although the human genome predominantly adopts the B DNA conformation, evidence indicates that non-B DNA forms exert significant influence on gene expression and disease development. This highlights the need for dedicated computational frameworks to systematically investigate these alternative structures. Machine learning model, encompassing supervised and unsupervised algorithms such as K Nearest Neighbors, Support Vector Machines, and deep learning architectures including Convolutional Neural Networks, have shown considerable potential in predicting sequence motifs predisposed to forming non-B DNA conformations. These predictive tools contribute to identifying genomic regions associated with disease susceptibility. Complementary bioinformatics platforms and molecular docking tools, notably Auto Dock, along with chemical libraries like ZINC, facilitate the virtual screening of small molecules targeting specific DNA structures. Stabilizers of G quadruplexes, exemplified by CX 5461, have demonstrated therapeutic promise in BRCA-deficient cancers, highlighting the translational impact of computational methods on drug discovery. Anticipating DNA structural shifts opens new avenues in personalized medicine for complex diseases, with computational chemistry and machine learning deepening our understanding of DNA topology and guiding smarter ligand design. The integrated approach proposed in this review addresses the previous studies performed in this field and highlights the current limitations in structural genomics and advances the development of precision therapeutics aligned with individual genomic profiles.