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Zeitschrift für Informatik und Systembiologie

ISSN: 0974-7230

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Volumen 16, Ausgabe 1 (2023)

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Developing a Data-driven Framework for Predicting Drug-Target Interactions Using Network Analysis and Machine Learning Techniques

Carole Antonio*

Drug discovery is a time-consuming and expensive process that relies on identifying compounds that interact with target proteins. In recent years, the use of network analysis and machine learning techniques has shown great promise in predicting drug-target interactions. In this paper, we present a data-driven framework for predicting drug-target interactions using network analysis and machine learning techniques. Our framework involves the construction of a drug-target interaction network and the use of various network analysis techniques to identify topological features that are indicative of drug-target interactions. We also use machine learning techniques to train a predictive model that can accurately predict drugtarget interactions. Our framework was evaluated on several benchmark datasets and demonstrated superior performance compared to existing state-of-the-art methods. We believe that our framework has the potential to significantly accelerate the drug discovery process.
Mini-Artikel

Exploring the Potential of Blockchain Technology for Secure and Efficient Sharing of Medical Data in Personalized Medicine

Matthieu Claure*

Personalized medicine is an emerging approach that aims to provide customized healthcare solutions based on a patient's unique genetic makeup and health history. However, the success of personalized medicine relies heavily on the availability of accurate and comprehensive medical data. The sharing of such data is often hindered by concerns over privacy, security, and interoperability. Block chain technology has emerged as a potential solution to these challenges by enabling secure and efficient sharing of medical data among authorized parties. This paper explores the potential of block chain technology for the sharing of medical data in personalized medicine. It examines the advantages of block chain technology, the challenges of implementing it in healthcare, and the potential use cases of block chain technology in personalized medicine.
Forschungsartikel

Involvement of Cissus populnea Derived Compounds in Phosphodiesterase Pathway in Erectile Dysfunction: In Silico Study

Moses Orimoloye Akinjiyan*, Olusola Olalekan Elekofehinti, Ayomide Precious Ajiboro, Elizabeth Foluke Awodire, Adeodotun Olayemi Oluwatuyi and Stephen Adeleke Adesida

Erectile Dysfunction (ED) has been a threat among couples and is one of the challenging disorders in Nigeria and the world. Various drugs targeting Phosphodiesterase 5 (PDE5) inhibition like Pyrazinopyridoindole (Tadalafil), have been used for the treatment of ED but, they are associated with side effects such as headache, diarrhea, back pain, and stomach upset among others. Medicinal plants are now being explored for the treatment of various diseases and disorders including ED because they are affordable with little or no side effects. Cissus populnea (CP) is a popular plant in Nigeria used in the management of ED but there is a paucity of information on the mechanisms involved. In this study, Schrodinger suites were employed for docking of thirty-eight CP phytocompounds gotten from HPLC analysis and works of literature against Phosphodiesterase PDE5, a key enzyme in the erection pathway. Seven leading compounds were found to have higher docking scores and binding affinity compared to Pyrazinopyridoindole (Tadalafil), with 9-octadecenoic acid, having the highest docking score of -13.078 Kcal/mol. The hit compounds were further subjected to ADME prediction. The findings suggested that C. populnea compounds are potential drug candidates with better hit than Tadalafil in managing ED and merit additional investigation.

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