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Updated: Jan 31, 2026

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Uncovering abnormal changes in logP after fluorination using molecular dynamics simulations.

Kai Liu1,2, Hironori Kokubo3,4

  • 1Medicinal Chemistry Research Laboratories, Pharmaceutical Research Division, Takeda Pharmaceutical Company Limited, 26-1, Muraoka-Higashi 2-chome, Fujisawa, Kanagawa, 251-8555, Japan.

Journal of Computer-Aided Molecular Design
|January 4, 2019
PubMed
Summary

Molecular dynamics simulations accurately predict how fluorination affects ligand logP values. This method, particularly useful for drug discovery, identifies fluorination-induced dipole changes as key to abnormal logP reductions.

Keywords:
FluorinationMolecular dynamicsSolvation free energy calculationlogP

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Area of Science:

  • Computational Chemistry
  • Drug Discovery
  • Molecular Modeling

Background:

  • Lipophilicity, quantified by logP (1-octanol/water partition coefficient), is crucial for drug properties.
  • Fluorination is a common strategy in drug design to modulate physicochemical and metabolic properties.
  • Predicting fluorination's impact on logP remains challenging.

Purpose of the Study:

  • To investigate fluorination-induced changes in ligand logP using molecular dynamics (MD) simulations.
  • To validate MD simulation protocols and force fields for logP calculations.
  • To understand the mechanism behind abnormal logP reduction upon fluorination in drug molecules.

Main Methods:

  • Molecular dynamics simulations were employed to calculate logP values.
  • Force field parameters (GAFF and GAFF2) were validated using n-alkanes and their halogenated analogs.
  • MD simulations were applied to test sets including alcohols, indoles, and the drug molecule TAK-438.

Main Results:

  • Calculated logP values showed good agreement with experimental data (RMSE of 0.61-0.68 log units).
  • MD simulations successfully reproduced the experimentally observed abnormal logP reduction for TAK-438.
  • Fluorination-induced dipole changes were identified as the primary cause of abnormal logP reduction.

Conclusions:

  • MD simulations offer a reliable strategy for predicting fluorination-induced logP changes.
  • This approach can aid in the rational design of drug candidates with desired lipophilicity profiles.
  • Understanding logP shifts is vital for optimizing drug discovery and development.