Authors : Suruthika V.; Dr. Elamathi Natarajan

Volume/Issue : Volume 11 - 2026, Issue 6 - June

Google Scholar : https://tinyurl.com/4kz643en

Scribd : https://tinyurl.com/mvdzets7

DOI : https://doi.org/10.38124/ijisrt/26jun753

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The emergence of artificial intelligence in the modern medicinal approach represents a revolutionary approach towards solving the shortcomings of conventional techniques employed in drug design and discovery. The application of ligand-based drug design (LBDD) relies on information obtained from already existing biologically active compounds to predict potential candidate molecules that possess required properties. Emerging trends in machine learning (ML), deep learning (DL), and computational chemistry have provided new ways of improving LBDD by making precise predictions about molecular parameters like their bioactivity, toxicity, and pharmacokinetics. QSAR modeling, molecular fingerprinting, virtual screening, molecular docking, and pharmacophore modeling have become essential elements of AI-based drug design techniques. In addition to this, there is an increased focus on using generative AI to develop new molecules through de novo design and lead optimization strategies. The current paper provides an overview of the most recent achievements in AI-assisted LBDD, including relevant computational methods, applications, advantages, and challenges.

Keywords : Artificial Intelligence; Ligand-Based Drug Design; Machine Learning ;Deep Learning; QSAR; Virtual Screening; Molecular Docking; Generative AI.

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