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CUSA and CUDE: GPU-accelerated methods for estimating solvent accessible surface area and desolvation.

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This study introduces a novel, differentiable model for desolvation energy calculation based on atomic positions. The algorithm achieves significant speedups using graphics processing units (GPUs), outperforming CPUs by up to 100 times.

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Area of Science:

  • Computational Chemistry
  • Molecular Modeling
  • Physical Chemistry

Background:

  • A known linear correlation exists between accessible surface areas and solvation energies.
  • Accurate calculation of desolvation energy is crucial for molecular simulations.

Purpose of the Study:

  • To derive a simple, differentiable model for desolvation energy.
  • To investigate the computational efficiency of the model using hardware acceleration.

Main Methods:

  • Utilized an analytic formula for solvent accessible surface area calculations.
  • Developed a differentiable function for desolvation energy based on atomic positions.
  • Implemented and tested the algorithm on graphics processing units (GPUs) and central processing units (CPUs).

Main Results:

  • A novel, differentiable model for desolvation energy was successfully derived.
  • The GPU implementation demonstrated a performance increase of up to two orders of magnitude compared to CPU.
  • The algorithm's scaling properties and applicability to general pairwise algorithms were explored.

Conclusions:

  • The derived desolvation energy model is efficient and suitable for GPU acceleration.
  • This approach offers a significant computational advantage for molecular simulations requiring desolvation energy calculations.
  • The findings provide implementation details for general pairwise algorithms.