Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland 21702, USA.
J Chem Inf Model. 2012 Feb 27;52(2):492-505. doi: 10.1021/ci2003544. Epub 2012 Jan 23.
Polypharmacology has emerged as a new theme in drug discovery. In this paper, we studied polypharmacology using a ligand-based target fishing (LBTF) protocol. To implement the protocol, we first generated a chemogenomic database that links individual protein targets with a specified set of drugs or target representatives. Target profiles were then generated for a given query molecule by computing maximal shape/chemistry overlap between the query molecule and the drug sets assigned to each protein target. The overlap was computed using the program ROCS (Rapid Overlay of Chemical Structures). We validated this approach using the Directory of Useful Decoys (DUD). DUD contains 2950 active compounds, each with 36 property-matched decoys, against 40 protein targets. We chose a set of known drugs to represent each DUD target, and we carried out ligand-based virtual screens using data sets of DUD actives seeded into DUD decoys for each target. We computed Receiver Operator Characteristic (ROC) curves and associated area under the curve (AUC) values. For the majority of targets studied, the AUC values were significantly better than for the case of a random selection of compounds. In a second test, the method successfully identified off-targets for drugs such as rimantadine, propranolol, and domperidone that were consistent with those identified by recent experiments. The results from our ROCS-based target fishing approach are promising and have potential application in drug repurposing for single and multiple targets, identifying targets for orphan compounds, and adverse effect prediction.
多靶标药物发现已成新药研发的新主题。本文采用基于配体的靶标钓取（LBTF）方案研究多靶标药物发现。为实施该方案，我们首先构建了一个将单个蛋白靶标与特定药物或靶标代表物相联系的化学生物基因组数据库。通过计算查询分子与分配给每个蛋白靶标的药物集之间的最大形状/化学重叠，为给定的查询分子生成靶标图谱。重叠通过程序 ROCS（快速化学结构重叠）进行计算。我们使用目录有用诱饵物（DUD）验证了该方法。DUD 包含 2950 个活性化合物，每个化合物有 36 个性质匹配的诱饵物，针对 40 个蛋白靶标。我们选择了一组已知药物来代表 DUD 的每个靶标，并针对每个靶标进行基于配体的虚拟筛选，方法是将 DUD 活性物数据集中的化合物播种到 DUD 诱饵物中。我们计算了接收者操作特征（ROC）曲线和相关曲线下面积（AUC）值。对于大多数研究的靶标，AUC 值明显优于随机化合物选择的情况。在第二个测试中，该方法成功地识别了利马烷、普萘洛尔和多潘立酮等药物的非靶标，与最近实验中确定的非靶标一致。我们基于 ROCS 的靶标钓取方法的结果很有前景，并且在单靶标和多靶标药物再利用、识别孤儿化合物的靶标以及不良效应预测方面具有潜在应用。
