Narykov Oleksandr, Zhu Yitan, Brettin Thomas, Evrard Yvonne A, Partin Alexander, Shukla Maulik, Xia Fangfang, Clyde Austin, Vasanthakumari Priyanka, Doroshow James H, Stevens Rick L
Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA.
Leidos Biomedical Research, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.
Cancers (Basel). 2023 Dec 21;16(1):50. doi: 10.3390/cancers16010050.
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，同一组织学类型的肿瘤对治疗的反应可能不同。抗癌药物反应预测对于药物开发和患者治疗方案设计都至关重要。尽管已经使用了各种计算方法和数据来开发药物反应预测模型，但由于癌症机制和癌症-药物相互作用的复杂性，这仍然是一个具有挑战性的问题。为了更好地表征癌症与药物之间的相互作用，我们研究了将计算得出的分子作用机制特征整合到预测模型中的可行性。具体而言，我们将药物分子与靶蛋白的对接分数与癌症基因表达及分子药物描述符相结合，以构建反应模型。通过对大型药物筛选数据的测试，结果表明，将对接分数作为附加特征添加时，药物反应预测性能有小幅提升。我们讨论了当前方法的局限性，并为研究界提供了一个抗癌药物大规模计算对接的基线数据集。
