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Systematic Investigation of Lipophilicity Modulation by Aliphatic Fluorination Motifs.

Benjamin JeffriesZhong WangHannah R FelsteadJean-Yves Le QuestelJames S ScottElisabetta ChiarparinJérôme GratonBruno Linclau
Published in: Journal of medicinal chemistry (2020)
Optimization of compound lipophilicity is a key aspect of drug discovery. The aim of this work was to compare the lipophilicity modulations induced by 16 distinct known and novel fluoroalkyl motifs on three parent models. Fifty fluorinated compounds, with 28 novel experimental aliphatic log P values, are involved in discussing various lipophilicity trends. As well as confirming known trends, a number of novel lipophilicity-reducing motifs are introduced. Tactics to reduce lipophilicity are discussed, such as "motif extensions" and "motif rearrangements", including with concomitant extension of the carbon chain, as well as one- and two-fluorine 'deletions' within perfluoroalkyl groups. Quantum chemical log P calculations (SMD-MN15) based on solvent-dependent three-dimensional (3D) conformational analysis gave excellent correlations with experimental values, superior to Clog P predictions based on 2D structural motifs. The availability of a systematic collection of data based on a small number of parent molecules illustrates the relative lipophilicity modulations of aliphatic fluorination motifs.
Keyphrases
  • drug discovery
  • molecular dynamics
  • molecular dynamics simulations
  • machine learning
  • positron emission tomography
  • electronic health record
  • big data
  • ionic liquid
  • monte carlo
  • data analysis