# Maxent-Stress Optimization of 3D Biomolecular Models

## TL;DR

This paper introduces a maxent-stress optimization method for translating noisy distance data into accurate 3D biomolecular structures, enabling faster and more reliable modeling for scientists.

## Contribution

It extends graph drawing techniques with new constraints and confidence values to improve 3D biomolecular structure inference from distance data.

## Key findings

- Produces models that match distance data well
- Handles noisy and error-prone data effectively
- Operates significantly faster than existing tools

## Abstract

Knowing a biomolecule's structure is inherently linked to and a prerequisite for any detailed understanding of its function. Significant effort has gone into developing technologies for structural characterization. These technologies do not directly provide 3D structures; instead they typically yield noisy and erroneous distance information between specific entities such as atoms or residues, which have to be translated into consistent 3D models. Here we present an approach for this translation process based on maxent-stress optimization. Our new approach extends the original graph drawing method for the new application's specifics by introducing additional constraints and confidence values as well as algorithmic components. Extensive experiments demonstrate that our approach infers structural models (i. e., sensible 3D coordinates for the molecule's atoms) that correspond well to the distance information, can handle noisy and error-prone data, and is considerably faster than established tools. Our results promise to allow domain scientists nearly-interactive structural modeling based on distance constraints.