J Chem Inf Model - Extraction of discontinuous structure-activity relationships from compound data sets through particle swarm optimization.

Tópicos

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Resumo

The characterization of structure-activity relationship (SAR) features of large compound data sets has been a hot topic in recent years, and different methods for large-scale SAR analysis have been introduced. The exploration of local SAR components and prioritization of compound subsets have thus far mostly relied on graphical analysis methods that capture similarity and potency relationships in a systematic manner. A currently unsolved problem in large-scale SAR analysis is how to automatically select those compound subsets from large data sets that carry most SAR information. For this purpose, we introduce a numerical optimization scheme that is based on particle swarm optimization guided by an SAR scoring function. The methodology is applied to four large compound sets. We demonstrate that compound subsets representing the most discontinuous local SARs are consistently selected through particle swarm optimization.

Resumo Limpo

character structureact relationship sar featur larg compound data set hot topic recent year differ method largescal sar analysi introduc explor local sar compon priorit compound subset thus far most reli graphic analysi method captur similar potenc relationship systemat manner current unsolv problem largescal sar analysi automat select compound subset larg data set carri sar inform purpos introduc numer optim scheme base particl swarm optim guid sar score function methodolog appli four larg compound set demonstr compound subset repres discontinu local sar consist select particl swarm optim

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