Weber Alexander, Teckentrup Andreas, Briem Hans
Department of Lead Discovery, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str. 65, D-88397 Biberach, Germany.
J Comput Aided Mol Des. 2002 Dec;16(12):903-16. doi: 10.1023/a:1023836420388.
Methods to describe the similarity of fragments occurring in drug-like molecules are of fundamental importance in computational drug design. In the early phase of lead discovery, they can help to select diverse building blocks for combinatorial compound libraries intended for broad screening. In lead optimization, such methods can guide bioisosteric replacements of one functional group by another or serve as descriptors for QSAR calculations. In this paper, we outline the development of a novel 3D descriptor, termed Flexsim-R, which is a further extension of our virtual affinity fingerprint idea. Descriptors are calculated based on docking of small fragments such as building blocks for combinatorial chemistry or functional groups of drug-like molecules into a reference panel of protein binding sites. The method is validated by examining the neighborhood behavior of the affinity fingerprints and by deriving predictive QSAR models for a couple of literature peptide data sets.
描述类药物分子中片段相似性的方法在计算机辅助药物设计中至关重要。在先导化合物发现的早期阶段，它们有助于为用于广泛筛选的组合化合物库选择多样的结构单元。在先导化合物优化阶段，此类方法可指导一个官能团被另一个官能团进行生物电子等排体替换，或用作定量构效关系（QSAR）计算的描述符。在本文中，我们概述了一种新型三维描述符Flexsim-R的开发，它是我们虚拟亲和力指纹概念的进一步扩展。描述符基于小分子片段（如组合化学的结构单元或类药物分子的官能团）与蛋白质结合位点参考面板的对接来计算。通过检查亲和力指纹的邻域行为以及推导几个文献肽数据集的预测QSAR模型，对该方法进行了验证。
