Detecting Drug Promiscuity Using Gaussian Ensemble Screening

Polypharmacology describes the binding of a ligand to multiple protein targets (a promiscuous ligand) or multiple diverse ligands binding to a given target (a promiscuous target). Pharmaceutical companies are discovering increasing numbers of both promiscuous drugs and drug targets. Hence, polypharmacology is now recognized as an important aspect of drug design. Here, we describe a new and fast way to predict polypharmacological relationships between drug classes quantitatively, which we call Gaussian Ensemble Screening (GES). This approach represents a cluster of molecules with similar spherical harmonic surface shapes as a Gaussian distribution with respect to a selected center molecule. Calculating the Gaussian overlap between pairs of such clusters allows the similarity between drug classes to be calculated analytically without requiring thousands of bootstrap comparisons, as in current promiscuity prediction approaches. We find that such cluster similarity scores also follow a Gaussian distribution. Hence, a cluster similarity score may be transformed into a probability value, or “p-value”, in order to quantify the relationships between drug classes. We present results obtained when using the GES approach to predict relationships between drug classes in a subset of the MDL Drug Data Report (MDDR) database. Our results indicate that GES is a useful way to study polypharmacology relationships, and it could provide a novel way to propose new targets for drug repositioning.

多药理学（Polypharmacology）指配体与多种蛋白靶点结合（即混杂配体），或是多种不同配体与同一靶点结合（即混杂靶点）。当前制药企业已发现越来越多的混杂药物与混杂药物靶点，因此多药理学现已被视为药物设计的重要研究方向。本文介绍一种可定量预测药物类别间多药理关系的新型快速方法，我们将其命名为高斯集成筛选（Gaussian Ensemble Screening, GES）。该方法将一组具有相似球谐表面形状的分子簇，以选定的中心分子为基准建模为高斯分布。通过计算此类簇对之间的高斯重叠度，可解析得到药物类别间的相似性评分，无需像当前的混杂性预测方法那样开展数千次自举比较。我们发现，此类簇相似性评分同样服从高斯分布，因此可将簇相似性评分转换为概率值（即“p值”），以量化药物类别间的关联程度。我们将该方法应用于MDL药物数据报告（MDL Drug Data Report, MDDR）数据库子集，以预测药物类别间的关联关系并得到相关结果。研究结果表明，高斯集成筛选是研究多药理学关联的有效手段，可为提出药物重定位的全新靶点提供新思路。