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Setting expectations in molecular optimizations: Strengths and limitations of commonly used composite parameters

机译:在分子优化中设定期望:常用复合参数的优缺点

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摘要

Over the past 15 years there have been extensive efforts to understand and reduce the high attrition rates of drug candidates with an increased focus on physicochemical properties. The fruits of this labor have been the generation of numerous efficiency indices, metric-based rules and visualization tools to help guide medicinal chemists in the design of new compounds with more favorable properties. This deluge of information may have had the unintended consequence of further obfuscating molecular optimizations by the inability of these scoring functions, rules and guides to reach a consensus on when a particular transformation is identified as beneficial. In this manuscript, several composite parameters, or efficiency indices, are examined utilizing theoretical and experimental matched molecular pair analyses in order to understand the basis for how each will perform under varying scenarios of molecular optimizations. In contrast to empirically derived composite parameters based on heavy atom count, lipophilic efficiency (LipE) sets consistent expectations regardless of molecular weight or relative potency and can be used to generate consistent expectations for any matched molecular pair.
机译:在过去的15年中,人们为了解和减少候选药物的高消耗率而付出了巨大的努力,而对物理化学性质的关注则越来越多。这项工作的成果是产生了许多效率指数,基于度量的规则和可视化工具,以帮助指导药物化学家设计性能更佳的新化合物。由于无法使用这些评分功能,规则和指南,无法就何时将特定转化确定为有利的共识达成共识,因此,大量的信息可能会带来意想不到的后果,即进一步混淆分子的优化。在本手稿中,使用理论和实验匹配的分子对分析来检查几个复合参数或效率指数,以了解每个参数在分子优化的不同情况下如何表现的基础。与根据经验得出的基于重原子数的复合参数相反,亲脂效率(LipE)设置了一致的期望值,而与分子量或相对效力无关,并且可以用于为任何匹配的分子对生成一致的期望值。

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