| Issue |
BIO Web Conf.
Volume 233, 2026
9th International Conference on Advances in Biosciences and Biotechnology: Emerging Innovations in Biomedical and Bioengineering Sciences (ICABB 2026)
|
|
|---|---|---|
| Article Number | 04005 | |
| Number of page(s) | 16 | |
| Section | Multi-Omics, Green Chemistry and Artificial Advancements in Biotechnology | |
| DOI | https://doi.org/10.1051/bioconf/202623304005 | |
| Published online | 23 April 2026 | |
The Role of Computational Chemistry and Cheminformatics in Modern Herbicide Discovery: A Critical Review
a Department of Applied Science, Krishna Institute of Engineering & Technology (KIET),Ghaziabad, Delhi-NCR, Uttar Pradesh, India- 201206
b Department of Chemistry, Lajpat Rai college, Sahibabad, Uttar Pradesh, India- 201005
c Department of Chemistry, Jaypee Institute of Information Technology, Noida-Sec 62.
* Email: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
Herbicides are prevalent chemical agents that can eliminate unwanted vegetation including weeds and specific grasses, or inhibit their growth to ensure the increase in agricultural productivity. The majority of pharmaceuticals drugs and herbicides are typically identified through a timeconsuming and expensive process of trial and error, which involves the evaluation of the potency of numerous compounds against a target in vitro. The tools of computational chemistry and techniques of cheminformatics acts as powerful tools that fasten the identification, design, and optimization of novel herbicidal molecules with increased cost-effectiveness. This comprehensive review addresses the current trends and challenges within herbicidal research along with illustrative examples including application in classical, cheminformatics and computational methodologies for herbicide innovation.
A broad literature survey conducted on the published literature on summarizing the various computational techniques and cheminformatics tools used in herbicide research including molecular docking (MolDock), molecular dynamics (MD) simulations, ADMET (Absorption Distribution Metabolism Excretion Toxicity) prediction, and machine learning (ML)-based QSAR (Quantitative structure analysis relationship) to evaluate their efficiency and restrictions in herbicide designing. This review forms a bridge between various computational tools and techniques with the herbicide designing in laboratory and cover the active challenges originated.
Key words: Computational chemistry / Cheminformatics / Herbicide discovery / Molecular modelling / QSAR / Virtual screening
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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