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Predicting Activity Cliffs with Free-Energy Perturbation.

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Predicting activity cliffs (ACs) in drug discovery is crucial. Free-energy perturbation (FEP) calculations accurately predict these large activity changes from small structural modifications, aiding lead optimization.

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Activity cliffs (ACs) represent significant structure-activity relationships in medicinal chemistry.
  • Predicting ACs is vital for efficient lead optimization in drug development.

Purpose of the Study:

  • To evaluate the performance of free-energy perturbation (FEP) calculations in predicting activity cliffs.
  • To assess FEP's accuracy for small structural modifications impacting biological activity.

Main Methods:

  • Free-energy perturbation (FEP) calculations were performed using Schrödinger-Desmond FEP+ and GROMACS software.
  • The predictive accuracy of FEP for activity cliffs was compared against experimental data.

Main Results:

  • FEP calculations predicted activity cliffs with an average error of 1.39 kcal/mol compared to experimental values.
  • Both FEP+ and GROMACS showed qualitative agreement, with FEP+ demonstrating better quantitative accuracy on one dataset.

Conclusions:

  • Well-implemented FEP calculations can accurately predict activity cliffs, offering a valuable tool for computational drug design.
  • FEP's ability to predict relative binding energy differences has profound implications for accelerating drug candidate optimization.