Steindl Theodora M, Schuster Daniela, Laggner Christian, Langer Thierry
Institute of Pharmacy, Computer Aided Molecular Design Group, University of Innsbruck, Innrain 52c, Austria.
J Chem Inf Model. 2006 Sep-Oct;46(5):2146-57. doi: 10.1021/ci6002043.
Parallel screening comprises a novel in silico method to predict the potential biological activities of a compound by screening it with a multitude of pharmacophore models. Our aim is to provide a fast, large-scale system that allows for virtual activity profiling. In this proof of principle study, carried out with the software tools LigandScout and Catalyst, we present a model work for the application of parallel pharmacophore-based virtual screening on a set of 50 structure-based pharmacophore models built for various viral targets and 100 antiviral compounds. The latter were screened against all pharmacophore models in order to determine if their biological targets could be correctly predicted via an enrichment of corresponding pharmacophores matching these ligands. The results demonstrate that the desired enrichment, that is, successful virtual activity profiling, was achieved for approximately 90% of all input molecules. We discuss descriptors for output validation, as well as various aspects influencing the analysis of the obtained activity profiles, and the effect of the utilized search modus for screening.
并行筛选包含一种新型的计算机模拟方法，通过用多种药效团模型对化合物进行筛选来预测其潜在的生物活性。我们的目标是提供一个快速、大规模的系统，以实现虚拟活性谱分析。在这项使用软件工具LigandScout和Catalyst进行的原理验证研究中，我们展示了一个模型工作，该模型将基于药效团的并行虚拟筛选应用于为各种病毒靶点构建的50个基于结构的药效团模型和100种抗病毒化合物。对这100种抗病毒化合物针对所有药效团模型进行筛选，以确定是否可以通过富集与这些配体匹配的相应药效团来正确预测其生物靶点。结果表明，大约90%的所有输入分子都实现了所需的富集，即成功的虚拟活性谱分析。我们讨论了用于输出验证的描述符，以及影响所获得活性谱分析的各个方面，以及用于筛选的搜索模式的效果。
