Data Centric Molecular Analysis and Evaluation of Hepatocellular Carcinoma Therapeutics Using Machine Intelligence-Based Tools.
Artificial intelligence
Drug discovery and repurposing
Hepatocellular carcinoma
Knowledge graphs
Machine learning
Journal
Journal of gastrointestinal cancer
ISSN: 1941-6636
Titre abrégé: J Gastrointest Cancer
Pays: United States
ID NLM: 101479627
Informations de publication
Date de publication:
Dec 2021
Dec 2021
Historique:
accepted:
13
11
2021
pubmed:
16
12
2021
medline:
17
2
2022
entrez:
15
12
2021
Statut:
ppublish
Résumé
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. 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. 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. 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.
Identifiants
pubmed: 34910274
doi: 10.1007/s12029-021-00768-x
pii: 10.1007/s12029-021-00768-x
doi:
Substances chimiques
Antineoplastic Agents
0
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
1266-1276Informations de copyright
© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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