Version 1
: Received: 10 October 2024 / Approved: 10 October 2024 / Online: 10 October 2024 (15:16:02 CEST)
How to cite:
Cit, A. Accelerating Drug Discovery: GPU-Enhanced Computational Biology Methods for Molecular Docking Simulations and Virtual Screening. Preprints2024, 2024100817. https://doi.org/10.20944/preprints202410.0817.v1
Cit, A. Accelerating Drug Discovery: GPU-Enhanced Computational Biology Methods for Molecular Docking Simulations and Virtual Screening. Preprints 2024, 2024100817. https://doi.org/10.20944/preprints202410.0817.v1
Cit, A. Accelerating Drug Discovery: GPU-Enhanced Computational Biology Methods for Molecular Docking Simulations and Virtual Screening. Preprints2024, 2024100817. https://doi.org/10.20944/preprints202410.0817.v1
APA Style
Cit, A. (2024). Accelerating Drug Discovery: GPU-Enhanced Computational Biology Methods for Molecular Docking Simulations and Virtual Screening. Preprints. https://doi.org/10.20944/preprints202410.0817.v1
Chicago/Turabian Style
Cit, A. 2024 "Accelerating Drug Discovery: GPU-Enhanced Computational Biology Methods for Molecular Docking Simulations and Virtual Screening" Preprints. https://doi.org/10.20944/preprints202410.0817.v1
Abstract
The drug discovery process is a complex and time-consuming endeavor that can greatly benefit from advancements in computational biology. This study explores the implementation of GPU-accelerated computational methods to enhance molecular docking simulations and virtual screening techniques, two crucial components of drug discovery. By leveraging the parallel processing capabilities of Graphics Processing Units (GPUs), we significantly accelerate the analysis of protein-ligand interactions and the identification of potential lead compounds. Our approach utilizes optimized algorithms and software frameworks to achieve substantial speedups over traditional CPU-based methods. Results demonstrate that GPU-accelerated molecular docking simulations and virtual screening can reduce computation times by up to 10-fold, enabling rapid and accurate identification of promising drug candidates. This accelerated workflow has the potential to revolutionize the drug discovery pipeline, facilitating the development of novel therapeutics and reducing the time and cost associated with bringing new treatments to market.
Biology and Life Sciences, Biology and Biotechnology
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.
