Version 1
: Received: 1 April 2024 / Approved: 1 April 2024 / Online: 1 April 2024 (13:35:22 CEST)
How to cite:
Niazi, S.K.; Mariam, Z.; Magoola, M. Optimizing Lead Compounds: The Role of Artificial Intelligence in Drug Discovery. Preprints2024, 2024040055. https://doi.org/10.20944/preprints202404.0055.v1
Niazi, S.K.; Mariam, Z.; Magoola, M. Optimizing Lead Compounds: The Role of Artificial Intelligence in Drug Discovery. Preprints 2024, 2024040055. https://doi.org/10.20944/preprints202404.0055.v1
Niazi, S.K.; Mariam, Z.; Magoola, M. Optimizing Lead Compounds: The Role of Artificial Intelligence in Drug Discovery. Preprints2024, 2024040055. https://doi.org/10.20944/preprints202404.0055.v1
APA Style
Niazi, S.K., Mariam, Z., & Magoola, M. (2024). Optimizing Lead Compounds: The Role of Artificial Intelligence in Drug Discovery. Preprints. https://doi.org/10.20944/preprints202404.0055.v1
Chicago/Turabian Style
Niazi, S.K., Zamara Mariam and Matthias Magoola. 2024 "Optimizing Lead Compounds: The Role of Artificial Intelligence in Drug Discovery" Preprints. https://doi.org/10.20944/preprints202404.0055.v1
Abstract
Lead optimization in drug discovery is a crucial phase where initial hits are refined into compounds with improved pharmacological properties. While traditional methods rely on manual experimentation and modifications, AI-driven techniques have revolutionized this process by leveraging big data and predictive modeling. This review explores how AI-driven approaches accelerate lead optimization, showcasing examples like deep neural networks and reinforcement learning. Integration of multi-omic data and experimental validation further enhances AI-driven strategies. The future lies in refining these AI methods, democratizing tools, and interdisciplinary collaboration to streamline drug discovery and address medical needs efficiently.
Keywords
Lead optimization; drug discovery; artificial intelligence; machine learning; deep neural networks; cheminformatics; chemical modifications; drug development
Subject
Computer Science and Mathematics, Mathematical and Computational Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
