School of Chemistry , University of Southampton , Highfield, Southampton SO17 1BJ , U.K.
CEISAM UMR CNRS 6230, Faculté des Sciences et des Techniques, Université de Nantes 2 , rue de la Houssinière - BP 92208 , 44322 Nantes Cedex 3, France.
J Med Chem. 2020 Feb 13;63(3):1002-1031. doi: 10.1021/acs.jmedchem.9b01172. Epub 2020 Jan 17.
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 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 calculations (SMD-MN15) based on solvent-dependent three-dimensional (3D) conformational analysis gave excellent correlations with experimental values, superior to log 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.
优化化合物的亲脂性是药物发现的一个关键方面。本工作的目的是比较 16 种不同的已知和新型氟烷基基序在三个母体模型上引起的亲脂性调制。涉及讨论各种亲脂性趋势的 50 种氟化化合物,具有 28 种新型实验脂值。除了证实已知的趋势外,还引入了一些新型的降低亲脂性的基序。讨论了降低亲脂性的策略,例如“基序扩展”和“基序重排”,包括同时延长碳链,以及在全氟烷基中进行一氟和二氟“删除”。基于溶剂依赖的三维(3D)构象分析的量子化学 log 计算(SMD-MN15)与实验值具有极好的相关性,优于基于 2D 结构基序的 log 预测。基于少量母体分子的系统收集数据的可用性说明了脂肪族氟化基序的相对亲脂性调制。