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Analog Accessibility Score (AAscore) for Rational Compound Selection.

Takato Ue1, Akinori Sato1,2, Tomoyuki Miyao1,2

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A new analog accessibility score (AAscore) was developed using in silico models to predict virtual analogs. This score helps prioritize compounds in drug discovery but showed limited correlation with experimentally synthesized analogs.

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Area of Science:

  • Computational chemistry
  • Medicinal chemistry
  • Drug discovery

Background:

  • Objective assessment of compound properties is crucial in drug discovery.
  • Existing in silico scores do not capture analog accessibility.
  • A reliable analog accessibility score is needed for virtual screening and hit-to-lead optimization.

Purpose of the Study:

  • To propose a novel analog accessibility score (AAscore) for evaluating compound synthesizability.
  • To utilize retrosynthesis and forward product prediction models for generating virtual analogs.
  • To assess the utility of AAscore in prioritizing compounds for analog synthesis.

Main Methods:

  • Development of an analog accessibility score (AAscore) based on virtual analog generation.
  • Application of retrosynthesis prediction and forward product prediction models.
  • Evaluation of AAscore against experimentally synthesized analogs using the compound-core relationship (CCR) method.
  • Analysis of AAscore's sensitivity to reactant database size.

Main Results:

  • The proposed AAscore quantifies unique virtual analogs and synthetic routes.
  • AAscore showed a weak correlation with the number of CCR-based synthesized analogs.
  • AAscore was significantly influenced by the number of available reactants in the database.
  • A case study demonstrated AAscore's ability to identify synthesizable compounds against carbonic anhydrase 2.

Conclusions:

  • AAscore offers a novel in silico approach to estimate analog accessibility.
  • The current AAscore requires further refinement to accurately predict experimental analog synthesis.
  • Optimizing reactant databases and prediction models can enhance AAscore's predictive power for drug discovery efforts.