Background: Advancements in pharmacogenomics, artificial intelligence (AI), and CRISPR gene-editing technology are revolutionizing precision medicine by enabling highly individualized therapeutic strategies. Artificial intelligence-driven computational techniques improve biomarker discovery and drug optimization while pharmacogenomics helps to identify genetic polymorphisms affecting medicine metabolism, efficacy, and toxicity. Genetically editing based on CRISPR presents a precise method for changing gene expression and repairing damaging mutations. This review explores the convergence of these three fields to enhance improved precision medicine. Method: A methodical study of the current literature was performed on the effects of pharmacogenomics on drug response variability, artificial intelligence, and CRISPR in predictive modeling and gene-editing applications. Results: Driven by artificial intelligence, pharmacogenomics allows clinicians to classify patients and select the appropriate medications depending on their DNA profiles. This reduces the side effect risk and increases the therapeutic efficacy. Precision genetic modifications made feasible by CRISPR technology improve therapy outcomes in oncology, metabolic illnesses, neurological diseases, and other fields. The integration of artificial intelligence streamlines genome-editing applications, lowers off-target effects, and increases CRISPR specificity. Notwithstanding these advances, issues including computational biases, moral dilemmas, and legal constraints still arise. Conclusions: The synergy of artificial intelligence, pharmacogenomics, and CRISPR alters precision medicine by letting customized therapeutic interventions. Clinically translating, however, hinges on resolving data privacy concerns, assuring equitable access, and strengthening legal systems. Future research should focus on refining CRISPR gene-editing technologies, enhancing AI-driven pharmacogenomics, and developing moral guidelines for applying these tools in individualized medicine going forward.

Background: Social determinants of health (SDOH) are critical contributors to cancer disparities, influencing prevention, early detection, treatment access, and survival outcomes. Addressing these disparities is essential in achieving equitable oncology care. Artificial intelligence (AI) is revolutionizing oncology by leveraging advanced computational methods to address SDOH-driven disparities through predictive analytics, data integration, and precision medicine. Methods: This review synthesizes findings from systematic reviews and original research on AI applications in cancer-focused SDOH research. Key methodologies include machine learning (ML), natural language processing (NLP), deep learning-based medical imaging, and explainable AI (XAI). Special emphasis is placed on AI’s ability to analyze large-scale oncology datasets, including electronic health records (EHRs), geographic information systems (GIS), and real-world clinical trial data, to enhance cancer risk stratification, optimize screening programs, and improve resource allocation. Results: AI has demonstrated significant advancements in cancer diagnostics, treatment planning, and survival prediction by integrating SDOH data. AI-driven radiomics and histopathology have enhanced early detection, particularly in underserved populations. Predictive modeling has improved personalized oncology care, enabling stratification based on socioeconomic and environmental factors. However, challenges remain, including AI bias in screening, trial underrepresentation, and treatment recommendation disparities. Conclusions: AI holds substantial potential to reduce cancer disparities by integrating SDOH into risk prediction, screening, and treatment personalization. Ethical deployment, bias mitigation, and robust regulatory frameworks are essential in ensuring fairness in AI-driven oncology. Integrating AI into precision oncology and public health strategies can bridge cancer care gaps, enhance early detection, and improve treatment outcomes for vulnerable populations.

We investigate the binding interactions of synthesized multi-walled carbon nanotubes (MWCNTs) with SARS-CoV-2 virus. Two essential components of the SARS-CoV-2 structure i.e. 6LU7 (main protease of SARS-CoV-2) and 6LZG (spike receptor-binding domain complexed with its receptor ACE2) were used for computational studies. MWCNTs of different morphologies (zigzag, armchair and chiral) were synthesized through a thermal chemical vapour deposition process as a function of pyrolysis temperature. A direct correlation between radius to volume ratio of the synthesized MWCNTs and the binding energies for all three (zigzag, armchair and chiral) conformations were observed in our computational studies. Our result suggests that MWCNTs interact with the active sites of the main protease along with the host angiotensin-converting enzyme2 (ACE2) receptors. Furthermore, it is also observed that MWCNTs have significant binding affinities towards SARS-CoV-2. However, the highest free binding energy of −87.09 kcal mol⁻¹ with 6LZG were shown by the armchair MWCNTs with SARS-CoV-2 through the simulated molecular dynamic trajectories, which could alter the SARS-CoV-2 structure with higher accuracy. The radial distribution function also confirms the density variation as a function of distance from a reference particle of MWCNTs for the study of interparticle interactions of the MWCNT and SARS-CoV-2. Due to these interesting attributes, such MWCNTs could find potential application in personal protective equipment (PPE) and diagnostic kits.

Our work investigates the interaction of synthesized graphene with the SARS-CoV-2 virus using molecular docking and molecular dynamics (MD) simulation method. The layer dependent inhibitory effect of graphene nanosheets on spike receptor-binding domain of 6LZG, complexed with host receptor i.e. angiotensin-converting enzyme 2 (ACE2) of SARS-CoV-2 was investigated through computational study. Graphene sample was synthesized using mechanical exfoliation with shear stress and its mechanism of inhibition towards the SARS-CoV-2 virus was explored by molecular docking and molecular dynamics (MD) simulation method. The thermodynamics study for the free binding energy of graphene towards the SARS-CoV-2 virus was analyzed. The binding energy of graphene towards the virus increased with an increasing number of layers. It shows the highest affinity of −17.5 Kcal/mol in molecular docking while ΔG_binding is in the order of −28.01 ± 0.04 5 Kcal/mol for the seven-layers structure. The increase in carbon layers is associated with an increasing number of edge sp³ –type carbon, providing greater curvature, further increase the surface reactivity responsible for high binding efficiency. The MD simulation data reveals the high inhibition efficiency of the synthesized graphene towards SARS-CoV-2 virus which would help to design future in-vitro studies. The graphene system could find potential applications in personal protective equipment and diagnostic kits. Communicated by Ramaswamy H. Sarma