Klimenko Kyrylo, Marcou Gilles, Horvath Dragos, Varnek Alexandre
Laboratoire de Chemoinformatique, UMR 7140 CNRS/Université de Strasbourg , 1, rue Blaise Pascal, Strasbourg 67000, France.
Department on Molecular Structure and Chemoinformatics, A.V. Bogatsky Physico-Chemical Institute of NAS of Ukraine , Lyustdorfskaya doroga, 86, Odessa 65080, Ukraine.
J Chem Inf Model. 2016 Aug 22;56(8):1438-54. doi: 10.1021/acs.jcim.6b00192. Epub 2016 Jul 25.
Curation, standardization and data fusion of the antiviral information present in the ChEMBL public database led to the definition of a robust data set, providing an association of antiviral compounds to seven broadly defined antiviral activity classes. Generative topographic mapping (GTM) subjected to evolutionary tuning was then used to produce maps of the antiviral chemical space, providing an optimal separation of compound families associated with the different antiviral classes. The ability to pinpoint the specific spots occupied (responsibility patterns) on a map by various classes of antiviral compounds opened the way for a GTM-supported search for privileged structural motifs, typical for each antiviral class. The privileged locations of antiviral classes were analyzed in order to highlight underlying privileged common structural motifs. Unlike in classical medicinal chemistry, where privileged structures are, almost always, predefined scaffolds, privileged structural motif detection based on GTM responsibility patterns has the decisive advantage of being able to automatically capture the nature ("resolution detail"-scaffold, detailed substructure, pharmacophore pattern, etc.) of the relevant structural motifs. Responsibility patterns were found to represent underlying structural motifs of various natures-from very fuzzy (groups of various "interchangeable" similar scaffolds), to the classical scenario in medicinal chemistry (underlying motif actually being the scaffold), to very precisely defined motifs (specifically substituted scaffolds).
对ChEMBL公共数据库中存在的抗病毒信息进行整理、标准化和数据融合，从而定义了一个可靠的数据集，该数据集将抗病毒化合物与七个广义定义的抗病毒活性类别相关联。然后，对经过进化调整的生成地形映射（GTM）进行分析，以生成抗病毒化学空间图谱，从而实现与不同抗病毒类别的化合物家族的最佳分离。通过各类抗病毒化合物确定在图谱上占据的特定位置（责任模式）的能力，为基于GTM的特权结构基序搜索开辟了道路，这些基序是每个抗病毒类别所特有的。分析了抗病毒类别的特权位置，以突出潜在的特权共同结构基序。与传统药物化学不同，在传统药物化学中，特权结构几乎总是预定义的支架，基于GTM责任模式的特权结构基序检测具有决定性优势，即能够自动捕捉相关结构基序的性质（“分辨率细节”——支架、详细子结构、药效团模式等）。研究发现，责任模式代表了各种性质的潜在结构基序——从非常模糊的（各种“可互换”的相似支架组），到药物化学中的经典情况（潜在基序实际上是支架），再到非常精确定义的基序（特定取代的支架）。
