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Quantum Mechanical-Cluster Approach to Solve the Bioisosteric Replacement Problem in Drug Design.

Timofey V Losev1,2,3, Igor S Gerasimov1,4, Maria V Panova1

  • 1N.D. Zelinsky Institute of Organic Chemistry of Russian Academy of Sciences, Leninsky prospect 47, 119991 Moscow, Russian Federation.

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Summary
This summary is machine-generated.

This study introduces a quantum mechanical (QM) cluster approach for predicting bioisosteric replacements in drug design. The new method accurately estimates changes in biological activity, improving upon existing computational techniques.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Medicinal chemistry

Background:

  • Bioisosteric replacements are crucial in drug design for optimizing compound properties.
  • Standard computational methods struggle to accurately predict binding affinities for similar bioisosteres due to their shape similarity.

Purpose of the Study:

  • To develop an advanced computational methodology for predicting the impact of bioisosteric substitutions on drug activity.
  • To specifically address the challenge of H → F replacements in drug design.

Main Methods:

  • A quantum mechanical (QM)-cluster approach utilizing the GFN2-xTB semi-empirical method was designed.
  • The methodology was applied to evaluate H → F bioisosteric replacements.

Main Results:

  • The QM-cluster approach achieved a standard deviation of 0.60 kcal/mol in predicting biological activity changes.
  • This performance surpasses the ChemPLP scoring function (0.83 kcal/mol) and approaches experimental accuracy (∼0.42 kcal/mol).

Conclusions:

  • The developed QM-cluster method offers accurate and efficient prediction of bioisosteric effects in drug design.
  • Its speed and lack of tunable parameters make it a valuable tool for modern drug research.