Tan Lu, Vogt Martin, Bajorath Jürgen
Department of Life Science Informatics, Rheinische Friedrich-Wilhelms-Universität Bonn, Germany.
Chem Biol Drug Des. 2009 Nov;74(5):449-56. doi: 10.1111/j.1747-0285.2009.00890.x. Epub 2009 Sep 28.
We introduce a computational scaling methodology that utilizes protein-ligand interaction information extracted from complex crystal structures to enrich similarity searching using structural fingerprints with compound class-specific information. Scaling factors are derived to emphasize fingerprint bit positions that result from interacting fragments of bound ligands and correspond to frequently occurring structural features. Through interaction-based scaling, this information is transferred to standard fingerprints of multiple reference compounds. In systematic search calculations, fingerprints scaled on the basis of three-dimensional information are found to produce higher recall rates of active compounds than alternative types of scaled and non-scaled fingerprints.
我们引入了一种计算缩放方法,该方法利用从复杂晶体结构中提取的蛋白质-配体相互作用信息,通过带有化合物类别特定信息的结构指纹来丰富相似性搜索。推导缩放因子以强调由结合配体的相互作用片段产生且对应于频繁出现的结构特征的指纹位位置。通过基于相互作用的缩放,此信息被转移到多个参考化合物的标准指纹中。在系统搜索计算中,发现基于三维信息缩放的指纹比其他类型的缩放和未缩放指纹能产生更高的活性化合物召回率。