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Data Centric Molecular Analysis and Evaluation of Hepatocellular Carcinoma Therapeutics Using Machine Intelligence-Based Tools

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Abstract

Purpose

Computational approaches have been used at different stages of drug development with the purpose of decreasing the time and cost of conventional experimental procedures. Lately, techniques mainly developed and applied in the field of artificial intelligence (AI), have been transferred to different application domains such as biomedicine.

Methods

In this study, we conducted an investigative analysis via data-driven evaluation of potential hepatocellular carcinoma (HCC) therapeutics in the context of AI-assisted drug discovery/repurposing. First, we discussed basic concepts, computational approaches, databases, modeling approaches, and featurization techniques in drug discovery/repurposing. In the analysis part, we automatically integrated HCC-related biological entities such as genes/proteins, pathways, phenotypes, drugs/compounds, and other diseases with similar implications, and represented these heterogeneous relationships via a knowledge graph using the CROssBAR system.

Results

Following the system-level evaluation and selection of critical genes/proteins and pathways to target, our deep learning-based drug/compound-target protein interaction predictors DEEPScreen and MDeePred have been employed for predicting new bioactive drugs and compounds for these critical targets. Finally, we embedded ligands of selected HCC-associated proteins which had a significant enrichment with the CROssBAR system into a 2-D space to identify and repurpose small molecule inhibitors as potential drug candidates based on their molecular similarities to known HCC drugs.

Conclusions

We expect that these series of data-driven analyses can be used as a roadmap to propose early-stage potential inhibitors (from database-scale sets of compounds) to both HCC and other complex diseases, which may subsequently be analyzed with more targeted in silico and experimental approaches.

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Availability of Data and Material

Results/data are available as supplementary data.

Code Availability

All of the utilized computational methods are available as tools/web-services/repositories. CROssBAR: https://crossbar.kansil.org and https://github.com/cansyl/CROssBAR, DEEPScreen: https://github.com/cansyl/DEEPScreen, MDeePred: https://github.com/cansyl/MDeePred, iBioProVis: https://ibpv.kansil.org/.

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Author information

Authors and Affiliations

  1. Section of Pulmonary and Critical Care Medicine, University of Chicago, Chicago, IL, 60637, USA

    Rengul Cetin-Atalay

  2. Cancer Systems Biology Laboratory, Graduate School of Informatics, METU, Ankara, 06800, Turkey

    Deniz Cansen Kahraman, Esra Nalbat, Heval Atas, M. Volkan Atalay, Aybar C. Acar & Tunca Doğan

  3. Department of Computer Engineering, Iskenderun Technical University, Iskenderun, Hatay, 31200, Turkey

    Ahmet Sureyya Rifaioglu

  4. Department of Computer Engineering, METU, Ankara, 06800, Turkey

    Ahmet Sureyya Rifaioglu, Ahmet Atakan, Ataberk Donmez & M. Volkan Atalay

  5. Department of Computer Engineering, EBYU, Ankara, 24002, Turkey

    Ahmet Atakan

  6. Department of Computer Science, University of Maryland, College Park, MD, 20742, USA

    Ataberk Donmez

  7. Department of Computer Engineering, Hacettepe University, Ankara, 06800, Turkey

    Tunca Doğan

Authors
  1. Rengul Cetin-Atalay
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  2. Deniz Cansen Kahraman
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Contributions

R. C. A., D. C. K., M. V. A., and T. D. conceived the idea and planned the work. A. S. R., A. A., A. D., H. A., R. C. A., V. A., and T. D. constructed the utilized methods and tools. R. C. A., D. C. K., and E. N. performed the data analysis. R. C. A., D. C. K., E. N., A. C. A., V. A., and T. D. wrote the manuscript. R. C. A., A. C. A., V. A., and T. D. supervised the overall study. All the authors have approved the manuscript.

Corresponding authors

Correspondence to Rengul Cetin-Atalay, Deniz Cansen Kahraman or Tunca Doğan.

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Cetin-Atalay, R., Kahraman, D.C., Nalbat, E. et al. Data Centric Molecular Analysis and Evaluation of Hepatocellular Carcinoma Therapeutics Using Machine Intelligence-Based Tools. J Gastrointest Canc 52, 1266–1276 (2021). https://doi.org/10.1007/s12029-021-00768-x

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