Enhancing Structural Bioinformatics with GPU-Accelerated Machine Learning

EasyChair Preprint no. 13987

Abstract

Structural bioinformatics, the study of the molecular structure of biological macromolecules, plays a crucial role in understanding cellular processes and developing therapeutic interventions. Traditional computational methods in this field often face challenges in handling the vast and complex datasets required for detailed structural analysis. GPU-accelerated machine learning offers a transformative approach to overcome these limitations, providing significant improvements in processing speed, accuracy, and scalability. This paper explores the integration of GPU-accelerated machine learning techniques in structural bioinformatics, highlighting their potential to enhance various applications, including protein structure prediction, molecular dynamics simulations, and drug discovery. By leveraging the parallel processing power of GPUs, we demonstrate substantial performance gains in data analysis and model training, enabling more sophisticated and real-time structural predictions. Our findings underscore the importance of adopting GPU-accelerated machine learning to advance the field of structural bioinformatics, paving the way for more efficient and precise biomedical research and applications.

Keyphrases: Graphics Processing Units, machine learning, structural bioinformatics

@Booklet{EasyChair:13987,
  author = {Abi Cit},
  title = {Enhancing Structural Bioinformatics with GPU-Accelerated Machine Learning},
  howpublished = {EasyChair Preprint no. 13987},

  year = {EasyChair, 2024}}