Sparse Representation of Gaussian Molecular Surface

TL;DR

This paper introduces a sparse representation method for Gaussian molecular surfaces using radial basis functions and L1 optimization, enabling accurate approximation with fewer basis functions for applications in molecular analysis.

Contribution

The paper presents a novel L1 optimization algorithm for sparse Gaussian surface representation, reducing basis functions needed for accurate modeling.

Findings

Achieves accurate surface approximation with fewer RBFs.

Demonstrates effectiveness in molecular structure alignment.

Applicable to general shape and molecular modeling tasks.

Abstract

In this paper, we propose a model and algorithm for sparse representing Gaussian molecular surface. The original Gaussian molecular surface is approximated by a relatively small number of radial basis functions (RBFs) with rotational ellipsoid feature. The sparsity of the RBF representation is achieved by solving a nonlinear $L_1$ optimization problem. Experimental results demonstrate that the original Gaussian molecular surface is able to be represented with good accuracy by much fewer RBFs using our $L_1$ model and algorithm. The sparse representation of Gaussian molecular surface is useful in various applications, such as molecular structure alignment, calculating molecular areas and volumes, and the method in principle can be applied to sparse representation of general shapes and coarse-grained molecular modeling.