University of Tübingen, Center for Bioinformatics Tübingen (ZBIT), Sand 1, 72076 Tübingen, Germany.
J Cheminform. 2011 Jul 6;3(1):23. doi: 10.1186/1758-2946-3-23.
The performance of 3D-based virtual screening similarity functions is affected by the applied conformations of compounds. Therefore, the results of 3D approaches are often less robust than 2D approaches. The application of 3D methods on multiple conformer data sets normally reduces this weakness, but entails a significant computational overhead. Therefore, we developed a special conformational space encoding by means of Gaussian mixture models and a similarity function that operates on these models. The application of a model-based encoding allows an efficient comparison of the conformational space of compounds.
Comparisons of our 4D flexible atom-pair approach with over 15 state-of-the-art 2D- and 3D-based virtual screening similarity functions on the 40 data sets of the Directory of Useful Decoys show a robust performance of our approach. Even 3D-based approaches that operate on multiple conformers yield inferior results. The 4D flexible atom-pair method achieves an averaged AUC value of 0.78 on the filtered Directory of Useful Decoys data sets. The best 2D- and 3D-based approaches of this study yield an AUC value of 0.74 and 0.72, respectively. As a result, the 4D flexible atom-pair approach achieves an average rank of 1.25 with respect to 15 other state-of-the-art similarity functions and four different evaluation metrics.
Our 4D method yields a robust performance on 40 pharmaceutically relevant targets. The conformational space encoding enables an efficient comparison of the conformational space. Therefore, the weakness of the 3D-based approaches on single conformations is circumvented. With over 100,000 similarity calculations on a single desktop CPU, the utilization of the 4D flexible atom-pair in real-world applications is feasible.
基于 3D 的虚拟筛选相似度函数的性能受到化合物应用构象的影响。因此,3D 方法的结果通常不如 2D 方法稳健。应用 3D 方法于多个构象数据集通常可以减少这一弱点,但需要大量的计算开销。因此,我们开发了一种特殊的构象空间编码方法,通过高斯混合模型和作用于这些模型的相似度函数来实现。基于模型的编码的应用允许对化合物的构象空间进行高效比较。
我们的 4D 柔性原子对方法与超过 15 种最先进的 2D 和 3D 基于虚拟筛选的相似度函数在 40 个目录有用诱饵数据集上的比较显示,我们的方法具有稳健的性能。即使是基于多个构象的 3D 方法也会产生较差的结果。4D 柔性原子对方法在过滤后的目录有用诱饵数据集上的平均 AUC 值为 0.78。本研究中最好的 2D 和 3D 方法的 AUC 值分别为 0.74 和 0.72。因此,4D 柔性原子对方法相对于 15 种其他最先进的相似度函数和四种不同的评估指标,平均排名为 1.25。
我们的 4D 方法在 40 个具有药物相关性的靶标上表现稳健。构象空间编码能够有效地比较构象空间。因此,规避了基于 3D 的方法在单个构象上的弱点。在单个桌面 CPU 上进行了超过 10 万次相似度计算,4D 柔性原子对在实际应用中的利用是可行的。