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Area of Science:

  • Computational Chemistry
  • Cheminformatics
  • Drug Discovery

Background:

  • Molecular descriptors are crucial for Bayesian optimization (BO) in chemical research.
  • Accessing structural and descriptor data for confidential compounds like pharmaceutical intermediates is challenging.
  • Quantum chemical calculations, while informative, are computationally intensive, especially for determining stable structures and electronic states.

Purpose of the Study:

  • To enhance the search performance of Bayesian optimization (BO) for confidential compounds.
  • To develop a computationally efficient method for generating molecular descriptors.
  • To investigate the impact of descriptor calculation methods on BO performance.

Main Methods:

  • Utilized density functional theory (DFT) for calculating molecular descriptors.
  • Computed descriptors using various combinations of basis sets and functionals.
  • Developed a method involving averaging multiple descriptor sets.
  • Applied this averaged descriptor dataset to Bayesian optimization.

Main Results:

  • Averaging multiple descriptor sets significantly improved BO search performance compared to single descriptor sets.
  • Increased the number of averaged descriptor sets led to progressively better BO search performance.
  • The developed method demonstrated a relatively small computational load.

Conclusions:

  • Averaging descriptors from multiple DFT calculations is an effective strategy to boost BO performance.
  • This approach offers a practical solution for optimizing confidential compounds where data is scarce.
  • The method is accessible to researchers without deep expertise in quantum chemical calculations.