Multi-Label Classification of Biological Targets Using Machine Learning Models for Enhanced Drug Discovery

Devarapati, Kesav Swaroop Reddy (2024) Multi-Label Classification of Biological Targets Using Machine Learning Models for Enhanced Drug Discovery. Masters thesis, Dublin, National College of Ireland.

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Abstract

The challenge of predicting multiple biological targets by means of chemical and biological features is addressed by this research, which is crucial in biomedical research and pharmaceutical development. Despite the great efforts in the drug discovery and personalized medicine, traditional approaches require lengthy and bloated approaches that are too costly. We apply multi-label classification techniques to predict target activations by using CatBoost and Gradient Boosting and Random Forest. We used a robust methodology that involved data preprocessing, exploring data and then modeling, and evaluating our model using an extensive set of metrics like accuracy, precision, recall and F1 Score. The results show how CatBoost outperforms all other methods in terms of accuracy and balanced metric performance. Our contribution to the state of the art is these findings which illustrate the potential of multi label models to solve complex biomedical problems. This work in practice can in turn stream line drug discovery processes, reduce drug development costs, and enable the delivery of more precise treatments by use of personalised medicine.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Hava Muntean, Cristina UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science R Medicine > RS Pharmacy and materia medica R Medicine > Healthcare Industry Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	02 Sep 2025 10:10
Last Modified:	02 Sep 2025 10:10
URI:	https://norma.ncirl.ie/id/eprint/8692

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