# Optimal measurement network of pairwise differences

## TL;DR

This paper analyzes how to optimally design measurement networks of pairwise differences to efficiently determine multiple quantities, with potential applications in computational chemistry and drug discovery.

## Contribution

It introduces a framework for optimizing measurement networks based on statistical criteria, improving the efficiency of estimating multiple quantities from pairwise data.

## Key findings

- Optimal networks significantly accelerate quantity determination.
- Network optimization reduces measurement costs while maintaining accuracy.
- Potential applications include drug binding free energy predictions.

## Abstract

When both the difference between two quantities and their individual values can be measured or computational predicted, multiple quantities can be determined from the measurements or predictions of select individual quantities and select pairwise differences. These measurements and predictions form a network connecting the quantities through their differences. Here, I analyze the optimization of such networks, where the trace ($A$-optimal), the largest eigenvalue ($E$-optimal), or the determinant ($D$-optimal) of the covariance matrix associated with the estimated quantities are minimized with respect to the allocation of the measurement (or computational) cost to different measurements (or predictions). My statistical analysis of the performance of such optimal measurement networks -- based on large sets of simulated data -- suggests that they substantially accelerate the determination of the quantities, and that they may be useful in applications such as the computational prediction of binding free energies of candidate drug molecules.