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Review Aug 2026

Artificial Intelligence-powered Advances in Phytomedicine: A Systematic Review on Plant-based Drug Discovery Software

Evaluated the role of AI-based software such as LEAFSNAP, Deep-Chem, PlantNet, Autodock, MestReNova, XCMS, ADMETlab, SynergyFinder, KEGG, TCMID, and Reactome in enhancing phytomedicine research by streamlining plant identification, bioactive com-pound discovery, molecular modeling, and pharmacokinetic profiling, thereb...

Himangi Vig, A. Wal, Zia Khanam et al. · 0 citations
Sep 2026

Molecular Engineering of Metallic Nanoparticle Using Natural Flavonoids: From Synthesis to Biological Evaluation.

INTRODUCTION Parkinson's disease (PD) is a progressive neurodegenerative disorder driven by oxidative stress, neuroinflammation, and mitochondrial dysfunction. Although flavonoids such as luteolin and fisetin exhibit strong antioxidant and neuroprotective properties, their therapeutic utility is limited by poor solubil...

Namra Aziz, Gagandeep Singh Basra, A. Wal et al. · 0 citations
Review Sep 2026

Artificial Intelligence-Driven Gene Delivery Systems in Dementia: A Comprehensive Review of Mechanisms, Tools, and Therapeutic Strategies.

AI has proven to be very helpful in predicting therapeutic targets, optimizing gene vectors, and customizing dementia treatment plans, and it also makes gene-target prediction, nanocarrier optimization, and post-treatment monitoring easier.

A. Wal, M. Shukla, Pranay Wal et al. · 0 citations
Review Sep 2026

Phytochemical Modulation of the p53 Tumor Suppressor Pathway: Mechanistic Insights, Translational Evidence, and Therapeutic Perspectives in Cancer

Cancer is a major global health challenge, with p53 mutations occurring in over 50% of human cancers and contributing to impaired DNA repair and apoptosis. In more recent work, phytochemicals in herbs such as curcumin, resveratrol, genistein, berberine, and withaferin A were found to enhance p53 activity or rescue...

Neha Verma, S. Chakraborty, Juhikaben Vaibhavkumar Patel et al. · 0 citations
Review Sep 2026

Harnessing the Power of Large Language Models for Drug Discovery: A Systematic Review of Current Applications and Future Directions.

INTRODUCTION The demand for inventive approaches to drug discovery has increased due to the rising costs, time, and failure rates in pharmaceutical research. Large Language Models (LLMs), with their sophisticated natural language processing and generative capabilities, have become potent instruments that have the poten...

Pranay Wal, M. Shukla, Kritika Sachan et al. · 0 citations
Review Aug 2026

Artificial Intelligence-Driven Diagnosis, Prediction, and Management of Polycystic Ovarian Disease (PCOD): A Comprehensive Systematic Review of Machine Learning, Deep Learning, and Emerging Technologies.

AI-based methods present enormous potential to revolutionise the diagnosis and management of PCOD by providing accurate, interpretable, and personalised care, and development of clinically interpretable models.

A. Wal, Mekala Moorthy, Rakesh Verma et al. · 0 citations
Sep 2026

Machine Learning in Hyperlipidaemia Research: Screening and Experimental Insights into Lipid Metabolism Modulators.

The combination of ML and molecular docking offers an efficient and accurate platform for antihyperlipidemic drug discovery, helping to overcome the limitations of currently available therapeutic strategies.

Ashish Srivastava, Mohit Prajapati, Parul Srivastava et al. · 0 citations
Review Jul 2026

A Survey Review of Computational Models and Their Applications in Predicting Drug-Drug Interactions

An overview of how artificial intelligence might serve not only as a tool but also as a strategic solution for safe prescribing and tailored pharmacotherapy, hence opening up new avenues for the field of drug safety science is presented.

D. Tripathi, A. Wal, Vivek Kumar Gupta et al. · 0 citations

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