Sidorov Pavel, Viira Birgit, Davioud-Charvet Elisabeth, Maran Uko, Marcou Gilles, Horvath Dragos, Varnek Alexandre
Laboratoire de Chemoinformatique, UMR7140 CNRS-Université de Strasbourg, 1 rue Blaise Pascal, 67000, Strasbourg, France.
Laboratory of Chemoinfomatics, Butlerov Institute of Chemistry, Kazan Federal University, Kazan, Russia, 420008.
J Comput Aided Mol Des. 2017 May;31(5):441-451. doi: 10.1007/s10822-017-0019-4. Epub 2017 Apr 3.
Generative topographic mapping (GTM) has been used to visualize and analyze the chemical space of antimalarial compounds as well as to build predictive models linking structure of molecules with their antimalarial activity. For this, a database, including ~3000 molecules tested in one or several of 17 anti-Plasmodium activity assessment protocols, has been compiled by assembling experimental data from in-house and ChEMBL databases. GTM classification models built on subsets corresponding to individual bioassays perform similarly to the earlier reported SVM models. Zones preferentially populated by active and inactive molecules, respectively, clearly emerge in the class landscapes supported by the GTM model. Their analysis resulted in identification of privileged structural motifs of potential antimalarial compounds. Projection of marketed antimalarial drugs on this map allowed us to delineate several areas in the chemical space corresponding to different mechanisms of antimalarial activity. This helped us to make a suggestion about the mode of action of the molecules populating these zones.
生成地形映射(GTM)已被用于可视化和分析抗疟化合物的化学空间,以及构建将分子结构与其抗疟活性联系起来的预测模型。为此,通过整合来自内部数据库和ChEMBL数据库的实验数据,汇编了一个数据库,其中包括在17种抗疟活性评估方案中的一种或几种中测试的约3000种分子。基于与单个生物测定相对应的子集构建的GTM分类模型的性能与早期报道的支持向量机(SVM)模型相似。在GTM模型支持的类别景观中,分别优先被活性分子和非活性分子占据的区域清晰可见。对这些区域的分析导致识别出潜在抗疟化合物的优势结构基序。将市售抗疟药物投影到这张地图上,使我们能够在化学空间中描绘出对应于不同抗疟活性机制的几个区域。这有助于我们对占据这些区域的分子的作用模式提出建议。