Numerical Experiments Bayessian Illumination

Numerical Experiments for Bayesian Illumination

This file contains the numerical experiments conducted for the manuscript titled “Bayesian Illumination: Inference and Quality-Diversity Accelerate Generative Molecular Models.” The experiments provide comprehensive benchmarking and validation of the Bayesian Illumination algorithm, which integrates Bayesian optimization with quality-diversity methods to improve molecular discovery.

The data includes:

• Descriptor-Based Rediscovery: Results from a novel benchmark where molecules are rediscovered based on conformer samples and descriptor-based representations (USRCAT and Zernike descriptors).
• Efficient Organic Photovoltaics: Performance metrics from multiple tasks, including maximising HOMO-LUMO gap values, minimising LUMO energy
  values, maximising the molecular dipole moment and maximising a combined efficiency score).
• Docking-Based Tasks: Outputs from docking-based optimizations, including stringent structural and physicochemical filtering to ensure realistic molecular designs. This also includes synthetic accessibility-adjusted docking scores.

This file serves as a supplement to the manuscript, providing the raw data and detailed performance metrics to support the reproducibility of the findings.