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

Date

2021-12-01

Author

Cetin-Atalay, Rengul
Kahraman, Deniz Cansen
Sinoplu, Esra
RİFAİOĞLU, AHMET SÜREYYA
Atakan, Ahmet
Dönmez, Ataberk
Atas, Heval
Atalay, Mehmet Volkan
Acar, Aybar C.
DOĞAN, TUNCA

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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.

Subject Keywords

Artificial intelligence, Machine learning, Knowledge graphs, Hepatocellular carcinoma, Drug discovery and repurposing, INFORMATION-SYSTEM, SORAFENIB, NETWORKS, CANCER

URI

https://hdl.handle.net/11511/95017

Journal

JOURNAL OF GASTROINTESTINAL CANCER

DOI

https://doi.org/10.1007/s12029-021-00768-x

Collections

Graduate School of Informatics, Article

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Citation Formats

R. Cetin-Atalay et al., “Data Centric Molecular Analysis and Evaluation of Hepatocellular Carcinoma Therapeutics Using Machine Intelligence-Based Tools,” JOURNAL OF GASTROINTESTINAL CANCER, pp. 0–0, 2021, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/95017.