J Chem Inf Model - Virtual screening data fusion using both structure- and ligand-based methods.

Tópicos

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{ sampl(1606) size(1419) use(1276) }
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Resumo

Virtual screening is widely applied in drug discovery, and significant effort has been put into improving current methods. In this study, we have evaluated the performance of compound ranking in virtual screening using five different data fusion algorithms on a total of 16 data sets. The data were generated by docking, pharmacophore search, shape similarity, and electrostatic similarity, spanning both structure- and ligand-based methods. The algorithms used for data fusion were sum rank, rank vote, sum score, Pareto ranking, and parallel selection. None of the fusion methods require any prior knowledge or input other than the results from the single methods and, thus, are readily applicable. The results show that compound ranking using data fusion improves the performance and consistency of virtual screening compared to the single methods alone. The best performing data fusion algorithm was parallel selection, but both rank voting and Pareto ranking also have good performance.

Resumo Limpo

virtual screen wide appli drug discoveri signific effort put improv current method studi evalu perform compound rank virtual screen use five differ data fusion algorithm total data set data generat dock pharmacophor search shape similar electrostat similar span structur ligandbas method algorithm use data fusion sum rank rank vote sum score pareto rank parallel select none fusion method requir prior knowledg input result singl method thus readili applic result show compound rank use data fusion improv perform consist virtual screen compar singl method alon best perform data fusion algorithm parallel select rank vote pareto rank also good perform

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