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Heuristic Global Optimization in Chemical Compound Space.

B Christopher Rinderspacher1

  • 1Materials Discovery and Technology Branch, US Army Research Laboratory, Aberdeen Proving Ground, Maryland 21005, United States.

The Journal of Physical Chemistry. A
|October 20, 2020
PubMed
Summary
This summary is machine-generated.

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Reordering strategies significantly improve combinatorial optimization in chemical space. Efficient global optimization (EGO) with a new linear-estimation ordering method outperforms heuristic next-neighbor search (HNNS), finding global optima with high probability.

Area of Science:

  • Computational Chemistry
  • Materials Science
  • Optimization Algorithms

Background:

  • Combinatorial optimization is crucial for discovering novel molecules with desired properties.
  • Existing methods like heuristic next-neighbor search (HNNS) face challenges in large chemical spaces.
  • Efficient global optimization (EGO) offers a promising alternative for constrained optimization problems.

Purpose of the Study:

  • To develop and evaluate advanced reordering strategies for combinatorial optimization in chemical space.
  • To compare the performance of kernel-based EGO with novel reordering against HNNS.
  • To analyze the impact of periodic kernels on EGO efficiency for electrochromic molecule databases.

Main Methods:

  • Implementation of sequential heuristic next-neighbor search (HNNS).

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  • Application of kernel-based efficient global optimization (EGO) with average ordering and a new linear-estimation ordering.
  • Analysis of modified Dirichlet, minimalist, and Gaussian-analogue periodic kernels for EGO.

    • Main Results:

    • Reordering strategies are critical for achieving high optimization efficiency in both average and worst-case scenarios.
    • The novel linear-estimation ordering paradigm demonstrates superior performance in the context of chemical optimization.
    • EGO, with optimized hyperparameters and algorithmic choices, surpasses HNNS in efficiency.
    • The global optimum for a small chemical problem was found with >98% likelihood using linear reordering heuristics.

    Conclusions:

    • Advanced reordering techniques, particularly the linear-estimation method, are essential for effective combinatorial optimization in chemical discovery.
    • Efficient global optimization (EGO) provides a powerful framework for navigating vast chemical spaces, outperforming traditional methods like HNNS.
    • The findings pave the way for accelerated discovery of functional molecules, such as electrochromic materials.