Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models

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.

doi:10.3390/cancers16010050

Title: Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models

Journal Article · 20 December 2023 · Cancers (Basel)

DOI: https://doi.org/10.3390/cancers16010050 · OSTI ID:2469954

^[1]; Zhu, Yitan ^[1]; Brettin, Thomas ^[1]; Evrard, Yvonne A. ^[2];

^[1]; Shukla, Maulik ^[1];

^[1]; Clyde, Austin ^[3];

^[1]; Doroshow, James H. ^[4]; Stevens, Rick L. ^[3]

Argonne National Laboratory (ANL), Argonne, IL (United States)
Frederick National Laboratory for Cancer Research, MD (United States)
Argonne National Laboratory (ANL), Argonne, IL (United States); The University of Chicago, IL (United States)
National Cancer Institute, Bethesda, MD (United States)

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.

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE; National Institutes of Health (NIH); Leidos Biomedical Research, Inc.

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 2469954

Journal Information:: Cancers (Basel), Journal Name: Cancers (Basel) Journal Issue: 1 Vol. 16; ISSN 2072-6694

Publisher:: MDPICopyright Statement

Country of Publication:: United States

Language:: English

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machine learning
molecular mechanisms of action
oncology

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