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

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


3D analysis of the binding sites for predicting binding affinities in drug design
Ataç, Ali Osman; Alpaslan, Ferda Nur; Büyükbingöl, Erdem; Department of Computer Engineering (2014)
Understanding the interaction between drug molecules and proteins is one of the main challenges in drug design. Several tools have been developed recently to decrease the complexity of the process. Artificial intelligence and machine learning methods have promising results in predicting the affinities. Recently, accurate estimations have been performed by extracting the electrostatic potentials from images of the drug-protein binding sites which were generated by autodocking simulator. In this study, a new ...
Data driven verification of synthetic gene networks
Aydın Göl, Ebru; Belta, Calin (2014-03-10)
Automatic design of synthetic gene networks with specific functions is an emerging field in synthetic biology. Quantitative evaluation of gene network designs is a missing feature of the existing automatic design tools. In this work, we address this issue and present a framework to probabilistically analyze the dynamic behavior of a gene network against specifications given in a rich and high level language. Given a gene network built from primitive DNA parts, and given experimental data for the parts, the ...
Reinvestigation of the synthetic and mechanistic aspects of Mn(III) acetate mediated oxidation of enones
Demir, Ayhan Sıtkı; Reis, O; Igdir, AC (Elsevier BV, 2004-04-05)
Mn(OAc)(3) mediated alpha'-acetoxylation of alpha,beta-unsaturated enones is reinvestigated from a synthetic and mechanistic point of view and an improved procedure based on the use of acetic acid as a co-solvent is presented. Excellent results were obtained for a variety of structurally diverse and synthetically important enones under the optimized conditions.
Deep Learning-Enabled Technologies for Bioimage Analysis
Rabbi, Fazle; Dabbagh, Sajjad Rahmani; Angın, Pelin; Yetisen, Ali Kemal; Tasoglu, Savas (2022-02-01)
Deep learning (DL) is a subfield of machine learning (ML), which has recently demon-strated its potency to significantly improve the quantification and classification workflows in bio-medical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of em...
Synthesis of new derivatives of boehmeriasin A and their biological evaluation in liver cancer
Guzelcan, Ece Akhan; Baxendale, Ian R.; Atalay, Rengül; Baumann, Marcus (Elsevier BV, 2019-03-15)
Two series of boehmeriasin A analogs have been synthesized in short and high yielding processes providing derivatives differing either in the alkaloid's pentacyclic scaffold or its peripheral substitution pattern. These series have enabled, for the first time, comparative studies into key biological properties revealing a new lead compound with exceptionally high activity against liver cancer cell lines in the picomolar range for both well (Huh7, Hep3B and HepG2) and poorly (Mahlavu, FOCUS and SNU475) diffe...
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: