Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
J Chem Inf Model. 2011 Aug 22;51(8):1840-7. doi: 10.1021/ci200242c. Epub 2011 Aug 5.
Chemical liabilities, such as adverse effects and toxicity, have a major impact on today's drug discovery process. In silico prediction of chemical liabilities is an important approach which can reduce costs and animal testing by complementing or replacing in vitro and in vivo liability models. There is a lack of integrated, extensible decision support systems for chemical liability assessment which run quickly and have easily interpretable results. Here we present a method which integrates similarity searches, structural alerts, and QSAR models which all are available from the Bioclipse workbench. Emphasis has been placed on interpretation of results, and substructures which are important for predictions are highlighted in the original chemical structures. This allows for interactively changing chemical structures with instant visual feedback and can be used for hypothesis testing of single chemical structures as well as compound collections. The system has a clear separation between methods and data, and the extensible architecture enables straightforward extension via addition of more plugins (such as new data sets and computational models). We demonstrate our method on three important safety end points: mutagenicity, carcinogenicity, and aryl hydrocarbon receptor (AhR) activation. Bioclipse and the decision support implementation are free, open source, and available from http://www.bioclipse.net/decision-support .
化学性质（如不良反应和毒性）对当今的药物发现过程有重大影响。计算机预测化学性质是一种重要的方法，可以通过补充或替代体外和体内性质模型来降低成本和减少动物试验。目前缺乏快速运行且结果易于解释的综合、可扩展的化学性质评估决策支持系统。在这里，我们提出了一种方法，该方法整合了相似性搜索、结构警示和定量构效关系模型，这些都可以从 Bioclipse 工作台获得。我们强调了结果的解释，并在原始化学结构中突出了对预测很重要的子结构。这允许通过即时视觉反馈交互式地改变化学结构，并可用于单个化学结构和化合物集合的假设检验。该系统在方法和数据之间有明确的分离，并且可扩展的架构通过添加更多插件（如新数据集和计算模型）来实现简单的扩展。我们在三个重要的安全性终点上演示了我们的方法：致突变性、致癌性和芳香烃受体 (AhR) 激活。Bioclipse 和决策支持实现都是免费的、开源的，可以从 http://www.bioclipse.net/decision-support 获得。
