J Chem Inf Model - Multiobjective particle swarm optimization: automated identification of structure-activity relationship-informative compounds with favorable physicochemical property distributions.

Tópicos

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Resumo

The selection of active compounds for chemical optimization efforts typically requires the consideration of multiple properties beyond potency. Herein we introduce a multiobjective particle swarm optimization approach to automatically extract compound subsets from large data sets that reveal structure-activity relationship (SAR) information and display physicochemical property distributions that are indicative of favorable absorption, distribution, metabolism, and excretion (ADME) characteristics. The approach is based on Pareto optimization of multiple objectives and does not require subjective intervention. It is automated and can be easily modified. We have applied the method to screen 10 compound data sets of different composition and global SAR phenotypes. In five of these data sets, between one and more than hundred compound subsets were identified that represented discontinuous local SARs and had desirable property distributions.

Resumo Limpo

select activ compound chemic optim effort typic requir consider multipl properti beyond potenc herein introduc multiobject particl swarm optim approach automat extract compound subset larg data set reveal structureact relationship sar inform display physicochem properti distribut indic favor absorpt distribut metabol excret adm characterist approach base pareto optim multipl object requir subject intervent autom can easili modifi appli method screen compound data set differ composit global sar phenotyp five data set one hundr compound subset identifi repres discontinu local sar desir properti distribut

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