ARCHIVES
VOL. 11, ISSUE 2 (2026)
AI-assisted discovery of drug molecules from insect natural products: Current progress, computational strategies, and future directions
Authors
Karan Sangale, Snehal K Bhavsar, Prasad Wangade, Aishwarya Kshirsagar, Laxmikant Borse
Abstract
Insects make up the most species-diverse animal group on Earth, and
they produce a wide variety of bioactive molecules — including antimicrobial
peptides, alkaloids, terpenoids, and venom proteins — that show meaningful
potential against cancer, infectious diseases, and inflammatory conditions. Yet
only a fraction of these compounds have been properly evaluated for drug
development, largely because traditional screening methods are slow and costly.
Artificial intelligence (AI), through its branches of machine learning (ML),
deep learning (DL), and generative modeling, has fundamentally changed how the
drug discovery pipeline works. When applied to insect-derived compounds, AI
allows researchers to navigate enormous chemical spaces, prioritize promising
candidates, and reduce experimental failure rates. This review covers the main
classes of bioactive insect compounds, the AI approaches used to study them,
key computational databases and tools, virtual screening strategies, ADMET
profiling, and real-world case studies. It also highlights a critical gap in
the field — there is currently no dedicated, AI-ready database for insect
natural products. Challenges like limited training data, model transparency,
and the need for experimental confirmation are addressed, along with future
directions involving generative drug design, multi-omics integration, and AI
agents [1-5].
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Pages:419-423
How to cite this article:
Karan Sangale, Snehal K Bhavsar, Prasad Wangade, Aishwarya Kshirsagar, Laxmikant Borse "AI-assisted discovery of drug molecules from insect natural products: Current progress, computational strategies, and future directions". International Journal of Entomology Research, Vol 11, Issue 2, 2026, Pages 419-423
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