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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Machine Learning Models for Predicting Drug-Target Interactions

Machine Learning Models for Predicting Drug-Target Interactions

Zuna, Arsen

Paperback

Networking

ISBN10: 338911596X
ISBN13: 9783389115961
Publisher: Grin Verlag
Published: Dec 13 2024
Pages: 46
Weight: 0.15
Height: 0.11 Width: 5.83 Depth: 8.27
Language: English
Bachelor Thesis from the year 2024 in the subject Computer Sciences - Artificial Intelligence, grade: 9, course: Data Science, Machine Learning, Artificial Intelligence, etc., language: English, abstract: The prediction of drug-target interactions stands as a pivotal task in drug discovery and repurposing endeavors. Traditional methods often struggle to capture the complexity inherent in these interactions. In this study, we explore the development of machine learning algorithms tailored to predict drug-target interactions. Leveraging datasets encompassing diverse information on chemical structures, protein sequences, and biological pathways associated with drug-target interactions, we embark on feature engineering endeavors to extract pertinent features from these heterogeneous data sources. Our investigation delves into various machine learning paradigms, including RF (Random Forests), SVP (Support Vector Machines), and NN (Neural Networks), aiming to exploit their capabilities in learning intricate patterns from multidimensional data. Through systematic experimentation and rigorous evaluation, we demonstrate the efficacy of our approach in accurately predicting drug-target interactions, thus offering a promising avenue to expedite drug discovery and repurposing efforts. Additionally, we discuss the interpretability of machine learning models and their role in elucidating the underlying mechanisms of drug-target interactions. Our research contributes to the advancement of computational methodologies in pharmaceutical research, fostering innovation and progress in predictive modeling for drug discovery. By harnessing the power of machine learning, we aspire to empower researchers with tools that streamline the drug development process, ultimately leading to improved patient outcomes and advancements in healthcare.

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