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Is Structure-Based Drug Design Ready for Selectivity Optimization?

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Summary

Free-energy calculations can predict compound selectivity between similar kinases, aiding drug discovery. Optimizing simulation length balances errors for accurate selectivity predictions.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Molecular modeling

Background:

  • Alchemical free-energy calculations (AFECs) are established for optimizing small-molecule potency.
  • Their application in predicting compound selectivity across targets remains less explored.
  • Binding site similarity may enhance AFEC accuracy for selectivity prediction due to error cancellation.

Purpose of the Study:

  • To evaluate the accuracy of AFECs for predicting selectivity of kinase inhibitors.
  • To investigate selectivity prediction between closely related kinases (CDK2/CDK9) and distantly related ones (CDK2/ERK2).
  • To analyze the impact of systematic error correlation on selectivity prediction accuracy.

Main Methods:

  • Utilized alchemical free-energy calculations for kinase inhibitor selectivity prediction.
  • Employed a Bayesian analysis approach to differentiate systematic and statistical errors.
  • Quantified the correlation of systematic errors across different kinase pairs.

Main Results:

  • High systematic error correlation between CDK2 and CDK9 suggests AFECs can significantly aid selectivity optimization.
  • For CDK2/ERK2, error correlation indicates potential for fortuitous cancellation even in less related kinases.
  • Longer simulations are beneficial for balancing statistical and systematic errors in selectivity predictions.

Conclusions:

  • AFECs show promise for guiding compound selectivity in drug discovery, especially between similar targets.
  • Understanding systematic error correlations is crucial for maximizing AFEC utility in selectivity prediction.
  • Simulation length optimization is key to leveraging AFECs for accurate selectivity predictions.