In silico methods for studying ankylosing spondylitis

Bidisha Das, Rojina Khatun, Malavika Bhattacharya*

Department of Biotechnology, Techno India University, West Bengal, Kolkata, India

Refer this article

Bidisha Das, Rojina Khatun, Malavika Bhattacharya, 2026. In silico methods for studying ankylosing spondylitis. Journal of medical pharmaceutical and allied sciences, V 15, I 5, Pages 26 – 34. Doi: https://doi.org/10.55522/jmpas.V15I5.7107.

ABSTRACT

The chronic inflammatory condition known as ankylosing spondylitis (AS) is an axial spondyloarthritis that causes pain, progressive spinal stiffness and, in advanced cases, ankylosis. Despite progress in clinical therapy (notably TNF and IL-17 inhibition), many patients have incomplete responses, side effects, or delayed diagnosis. In silico methods — spanning target discovery (genomics, GWAS), drug design based on structure (molecular docking, molecular dynamics), ligand-based methods (QSAR, pharmacophores), network pharmacology, and machine learning (ML)/AI approaches — are accelerating hypothesis generation, screening and translational research for AS. This review summarizes the biology and genetic architecture of AS that underpin computational work, details current in silico methods, surveys representative applications (including network pharmacology and docking studies on anti-inflammatory phytochemicals and small molecules), highlights strengths and limitations, and outlines ethical and humanitarian considerations for deploying computational discoveries in patient care.

Keywords:

Ankylosing spondylitis, Axial spondyloarthritis, In silico methods, Target discovery, Artificial intelligence.

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