Real-Time Protein Design Using GPU-Enhanced Computational Biology Techniques

EasyChair Preprint 14113

Abstract

The field of protein design is experiencing a transformative shift, driven by the integration of Graphics Processing Units (GPUs) in computational biology. This paper delves into the advancements in real-time protein design facilitated by GPU-enhanced computational techniques. Traditional protein design methods, constrained by extensive computational demands and prolonged processing times, are being revolutionized by the parallel processing power of GPUs, which significantly accelerates complex biological computations.

Our investigation focuses on the principles and mechanisms underlying GPU acceleration, highlighting its impact on the efficiency and precision of protein design processes. By leveraging the massive parallelism of GPUs, researchers can perform simulations and iterative refinements of protein structures in real-time, leading to more rapid and accurate predictions of protein folding, stability, and interactions. The integration of machine learning algorithms with GPU technology further enhances these capabilities, enabling the analysis of extensive biological datasets and the identification of novel protein configurations with unprecedented speed.

Keyphrases: Computational Biology Techniques, Graphics Processing Units (GPUs), protein design

@booklet{EasyChair:14113,
  author    = {Abi Litty},
  title     = {Real-Time Protein Design Using GPU-Enhanced Computational Biology Techniques},
  howpublished = {EasyChair Preprint 14113},
  year      = {EasyChair, 2024}}