Cancer is a heterogeneous disease in that tumors of the same histology type can respond differently to a treatment. Anti-cancer drug response prediction is of paramount importance for both drug development and patient treatment design. Although various computational methods and data have been used to develop drug response prediction models, it remains a challenging problem due to the complexities of cancer mechanisms and cancer-drug interactions. To better characterize the interaction between cancer and drugs, we investigate the feasibility of integrating computationally derived features of molecular mechanisms of action into prediction models. Specifically, we add docking scores of drug molecules and target proteins in combination with cancer gene expressions and molecular drug descriptors for building response models. The results demonstrate a marginal improvement in drug response prediction performance when adding docking scores as additional features, through tests on large drug screening data. We discuss the limitations of the current approach and provide the research community with a baseline dataset of the large-scale computational docking for anti-cancer drugs.
癌症是一种异质性疾病，即使组织学类型相同的肿瘤对同一治疗也可能产生不同反应。抗癌药物反应预测对于药物开发和患者治疗方案设计至关重要。尽管已有多种计算方法和数据被用于开发药物反应预测模型，但由于癌症机制及癌症-药物相互作用的复杂性，这仍是一个具有挑战性的难题。为更好地表征癌症与药物间的相互作用，本研究探讨了将分子作用机制的计算衍生特征整合至预测模型的可行性。具体而言，我们在构建反应模型时，将药物分子与靶蛋白的对接评分与癌症基因表达及分子药物描述符相结合。通过对大规模药物筛选数据的测试，结果显示添加对接评分作为附加特征后，药物反应预测性能获得边际提升。我们讨论了当前方法的局限性，并为研究界提供了大规模抗癌药物计算对接的基准数据集。
Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models
肿瘤(癌症)患者之家