Accelerating Drug Discovery with GPU-Powered Machine Learning: a Case Study in [Specific Disease Area]

EasyChair Preprint 14386

Abstract

The advent of GPU-powered machine learning has revolutionized the field of drug discovery, offering unprecedented computational speed and accuracy in analyzing complex biological data. This study explores the application of GPU-accelerated machine learning techniques in the discovery of novel therapeutics for [Specific Disease Area]. By leveraging the parallel processing capabilities of GPUs, we developed and implemented advanced predictive models that can rapidly identify potential drug candidates, significantly reducing the time and cost associated with traditional drug discovery methods. The case study highlights the integration of deep learning algorithms with large-scale biological datasets, including genomic, proteomic, and molecular interaction data, to predict the efficacy and safety profiles of candidate compounds. Our results demonstrate that GPU-accelerated models not only enhance the precision of drug-target interactions but also enable real-time analysis and optimization of chemical properties, paving the way for more efficient and targeted drug development. This approach represents a paradigm shift in drug discovery, where the synergistic use of GPU technology and machine learning algorithms can accelerate the transition from bench to bedside, ultimately improving patient outcomes in [Specific Disease Area].

Keyphrases: Central Processing Units (CPUs), Graphics Processing Units (GPUs), Machine Learning (ML)

@booklet{EasyChair:14386,
  author    = {Abey Litty},
  title     = {Accelerating Drug Discovery with GPU-Powered Machine Learning: a Case Study in [Specific Disease Area]},
  howpublished = {EasyChair Preprint 14386},
  year      = {EasyChair, 2024}}