Machine Learning Models for Predicting Drug-Target Interactions
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Sprache:Englisch
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Nein
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Erscheinungsdatum
07.03.2025
Verlag
GRINSeitenzahl
41 (Printausgabe)
Dateigröße
792 KB
Sprache
Englisch
EAN
9783389115954
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.
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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