GLIMPS: A Machine Learning Approach to Resolution Transformation for Multiscale Modeling
Keverne A. Louison, Ian L. Dryden, Charles A. Laughton
Abstract
We describe a general approach to transforming molecular models between different levels of resolution, based on machine learning methods. The approach uses a matched set of models at both levels of resolution for training, but requires only the coordinates of their particles and no side information (e.g., templates for substructures, defined mappings, or molecular mechanics force fields). Once trained, the approach can transform further molecular models of the system between the two levels of resolution in either direction with equal facility.
Topics & Concepts
Resolution (logic)Computer scienceTransformation (genetics)Set (abstract data type)Artificial intelligenceLow resolutionMachine learningHigh resolutionAlgorithmChemistryProgramming languageBiochemistryGeologyGeneRemote sensingMachine Learning in Materials ScienceProtein Structure and DynamicsComputational Drug Discovery Methods