Compared to traditional experimental approaches, computational modeling is a promising strategy to efficiently prioritize new candidates with low cost. In this study, we developed a novel data mining and computational modeling workflow proven to be applicable by screening new analgesic opioids. To this end, a large opioid data set was used as the probe to automatically obtain bioassay data from the PubChem portal. There were 114 PubChem bioassays selected to build quantitative structure–activity relationship (QSAR) models based on the testing results across the probe compounds. The compounds tested in each bioassay were used to develop 12 models using the combination of three machine learning approaches and four types of chemical descriptors. The model performance was evaluated by the coefficient of determination (R²) obtained from 5-fold cross-validation. In total, 49 models developed for 14 bioassays were selected based on the criteria and were identified to be mainly associated with binding affinities to different opioid receptors. The models for these 14 bioassays were further used to fill data gaps in the probe opioids data set and to predict general drug compounds in the DrugBank data set. This study provides a universal modeling strategy that can take advantage of large public data sets for computer-aided drug design (CADD).

中文翻译：

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虚拟阿片类药物生物特征的构建：一项数据驱动的 QSAR 建模研究以识别新的镇痛阿片类药物

与传统的实验方法相比，计算建模是一种很有前途的策略，可以以低成本有效地优先考虑新的候选者。在这项研究中，我们开发了一种新的数据挖掘和计算建模工作流程，该工作流程通过筛选新的镇痛阿片类药物被证明是适用的。为此，使用大型阿片类药物数据集作为探针，从 PubChem 门户自动获取生物测定数据。根据探针化合物的测试结果，选择了 114 种 PubChem 生物测定来构建定量构效关系 (QSAR) 模型。在每个生物测定中测试的化合物用于开发 12 个模型，结合使用三种机器学习方法和四种类型的化学描述符。模型性能通过决定系数（R² ) 从 5 折交叉验证中获得。总共根据标准选择了为 14 种生物测定开发的 49 种模型，并确定它们主要与对不同阿片受体的结合亲和力有关。这 14 种生物测定的模型进一步用于填补探针阿片类药物数据集中的数据空白，并预测 DrugBank 数据集中的一般药物化合物。这项研究提供了一种通用的建模策略，可以利用大型公共数据集进行计算机辅助药物设计 (CADD)。