报告题目：Molecular Sparse Representation by a 3D Ellipsoid Radial Basis Function Neural Network via L1 Regularization

报 告 人：桂升博士后（香港大学）

报告时间：2024年11月8日  14:30-16:00

报告地点：腾讯会议（693-218-959）

报告摘要：

The three-dimensional structures and shapes of biomolecules provide essential information about their interactions and functions. Unfortunately, the computational cost of biomolecular shape representation is an active challenge which increases rapidly as the number of atoms increase. Recent developments in sparse representation and deep learning have shown significant improvements in terms of time and space. A sparse representation of molecular shape is also useful in various other applications, such as molecular structure alignment, docking, and coarse-grained molecular modeling. We have developed an ellipsoid radial basis function neural network (ERBFNN) and an algorithm for sparsely representing molecular shape. To evaluate a sparse representation model of molecular shape, the Gaussian density map of the molecule is approximated using ERBFNN with a relatively small number of neurons. The deep learning models were trained by optimizing a nonlinear loss function with L1 regularization. Experimental results reveal that our algorithm can represent the original molecular shape with a relatively higher accuracy and fewer scale of ERBFNN. Our network in principle is applicable to the multiresolution sparse representation of molecular shape and coarse-grained molecular modeling.

报告人简介：

桂升，香港大学MILES荣誉博士后研究员。2021年博士毕业于中国科学院数学与系统科学研究院计算数学与科学工程计算研究所计算数学专业。主要研究方向包括：生物分子网格生成与优化技术、机器学习、偏微分方程数值解，目前已在SIAM J. Appl. Math., J. Comput. Phys., J. Phys. Chem. B, J. Chem. Inf. Model.等国际期刊发表多篇论文。