Laboratory for Chemometrics and Molecular Modeling, Chemistry Department, University of Perugia, Via Elce di Sotto, 10, 06123 Perugia, Italy.
J Comput Aided Mol Des. 2012 Nov;26(11):1247-66. doi: 10.1007/s10822-012-9612-8. Epub 2012 Oct 12.
FLAP fingerprints are applied in the ligand-, structure- and pharmacophore-based mode in a case study on antagonists of all four adenosine receptor (AR) subtypes. Structurally diverse antagonist collections with respect to the different ARs were constructed by including binding data to human species only. FLAP models well discriminate "active" (=highly potent) from "inactive" (=weakly potent) AR antagonists, as indicated by enrichment curves, numbers of false positives, and AUC values. For all FLAP modes, model predictivity slightly decreases as follows: A(2B)R > A(2A)R > A(3)R > A(1)R antagonists. General performance of FLAP modes in this study is: ligand- > structure- > pharmacophore- based mode. We also compared the FLAP performance with other common ligand- and structure-based fingerprints. Concerning the ligand-based mode, FLAP model performance is superior to ECFP4 and ROCS for all AR subtypes. Although focusing on the early first part of the A(2A), A(2B) and A(3) enrichment curves, ECFP4 and ROCS still retain a satisfactory retrieval of actives. FLAP is also superior when comparing the structure-based mode with PLANTS and GOLD. In this study we applied for the first time the novel FLAPPharm tool for pharmacophore generation. Pharmacophore hypotheses, generated with this tool, convincingly match with formerly published data. Finally, we could demonstrate the capability of FLAP models to uncover selectivity aspects although single AR subtype models were not trained for this purpose.
FLAP 指纹图谱以配体、结构和药效基团为基础模式应用于四种腺苷受体 (AR) 亚型拮抗剂的案例研究中。通过仅包括对人类物种的结合数据，构建了对不同 AR 具有不同结构多样性的拮抗剂集合。FLAP 模型很好地区分了“活性”（=高活性）和“非活性”（=低活性）AR 拮抗剂，这表明富集曲线、假阳性数量和 AUC 值都有所提高。对于所有的 FLAP 模式，模型预测性都略有下降，具体顺序如下：A(2B)R > A(2A)R > A(3)R > A(1)R 拮抗剂。在这项研究中，FLAP 模式的总体性能为：配体- > 结构- > 药效基团为基础的模式。我们还比较了 FLAP 性能与其他常见的配体和结构指纹图谱。在配体为基础的模式中，对于所有 AR 亚型，FLAP 模型的性能均优于 ECFP4 和 ROCS。尽管 ECFP4 和 ROCS 仅关注 A(2A)、A(2B)和 A(3)的早期富集曲线的前半部分，但仍能很好地检索到活性化合物。与 PLANTS 和 GOLD 相比，FLAP 在结构为基础的模式中也具有优势。在这项研究中，我们首次应用了新型的 FLAPPharm 工具来生成药效基团假说。该工具生成的药效基团假说与以前发表的数据非常吻合。最后，尽管没有针对单一 AR 亚型模型进行训练，但我们能够证明 FLAP 模型能够揭示选择性方面的能力。
