Laboratory for Molecular Design and Pharmaceutical Biophysics, Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, Eberhard Karls University Tuebingen, Auf der Morgenstelle 8, 72076 Tuebingen, Germany.
J Chem Inf Model. 2011 Oct 24;51(10):2650-65. doi: 10.1021/ci2001549. Epub 2011 Aug 18.
For widely applied in silico screening techniques success depends on the rational selection of an appropriate method. We herein present a fast, versatile, and robust method to construct demanding evaluation kits for objective in silico screening (DEKOIS). This automated process enables creating tailor-made decoy sets for any given sets of bioactives. It facilitates a target-dependent validation of docking algorithms and scoring functions helping to save time and resources. We have developed metrics for assessing and improving decoy set quality and employ them to investigate how decoy embedding affects docking. We demonstrate that screening performance is target-dependent and can be impaired by latent actives in the decoy set (LADS) or enhanced by poor decoy embedding. The presented method allows extending and complementing the collection of publicly available high quality decoy sets toward new target space. All present and future DEKOIS data sets will be made accessible at www.dekois.com.
广泛应用的计算筛选技术的成功取决于合理选择适当的方法。我们在此提出了一种快速、通用且稳健的方法来构建用于客观计算筛选的严格评估试剂盒（DEKOIS）。该自动化过程能够为任何给定的生物活性物质集创建定制的诱饵集。它有助于依赖于目标的对接算法和评分函数的验证，有助于节省时间和资源。我们已经开发了用于评估和改进诱饵集质量的指标，并将其用于研究诱饵嵌入如何影响对接。我们证明了筛选性能是依赖于目标的，并且可能会受到诱饵集中潜在活性物质（LADS）的影响，或者通过较差的诱饵嵌入而得到增强。所提出的方法允许将公开可用的高质量诱饵集扩展和补充到新的靶标空间。所有现有的和未来的 DEKOIS 数据集将可在 www.dekois.com 上获得。
