Version 1
: Received: 15 March 2024 / Approved: 15 March 2024 / Online: 15 March 2024 (19:11:07 CET)
How to cite:
Mariam, Z.; Niazi, S.K.; Magoola, M. Unlocking the Future of Drug Development: Generative AI, Digital Twins, and Beyond. Preprints2024, 2024030919. https://doi.org/10.20944/preprints202403.0919.v1
Mariam, Z.; Niazi, S.K.; Magoola, M. Unlocking the Future of Drug Development: Generative AI, Digital Twins, and Beyond. Preprints 2024, 2024030919. https://doi.org/10.20944/preprints202403.0919.v1
Mariam, Z.; Niazi, S.K.; Magoola, M. Unlocking the Future of Drug Development: Generative AI, Digital Twins, and Beyond. Preprints2024, 2024030919. https://doi.org/10.20944/preprints202403.0919.v1
APA Style
Mariam, Z., Niazi, S.K., & Magoola, M. (2024). Unlocking the Future of Drug Development: Generative AI, Digital Twins, and Beyond. Preprints. https://doi.org/10.20944/preprints202403.0919.v1
Chicago/Turabian Style
Mariam, Z., Sarfaraz K. Niazi and Matthias Magoola. 2024 "Unlocking the Future of Drug Development: Generative AI, Digital Twins, and Beyond" Preprints. https://doi.org/10.20944/preprints202403.0919.v1
Abstract
This article delves into the intersection of generative AI and digital twins within drug discovery, exploring their synergistic potential to revolutionize pharmaceutical research and development. Through various instances and examples, we illuminate how generative AI algorithms, capable of simulating vast chemical spaces and predicting molecular properties, are increasingly integrated with digital twins of biological systems to expedite drug discovery. By harnessing the power of computational models and machine learning, researchers can design novel compounds tailored to specific targets, optimize drug candidates, and simulate their behavior within virtual biological environments. This paradigm shift offers unprecedented opportunities for accelerating drug development, reducing costs, and, ultimately, improving patient outcomes. As we navigate this rapidly evolving landscape, collaboration between interdisciplinary teams and continued innovation will be paramount in realizing the promise of generative AI and digital twins in advancing drug discovery.
Keywords
generative AI; drug development; digital twins; prospective analysis
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
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.
