Drug-likeness Prediction and Fragment Extraction using Transformer-based Graph Neural Network on Traditional Chinese Medicine Molecules

Marco Colangelo

Drug-likeness Prediction and Fragment Extraction using Transformer-based Graph Neural Network on Traditional Chinese Medicine Molecules.

Rel. Stefano Di Carlo, Alessandro Savino, Roberta Bardini, Riccardo Smeriglio. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2024

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Abstract

The use of Traditional Chinese Medicine spans thousands of years, yet its integration into modern pharmaceutical research has been limited. A major challenge is the lack of systematic evaluation of the chemical properties of TCM compounds, which slows their development into approved pharmaceuticals. Adopting drug-likeness as a metric, which refers to the physicochemical and structural properties of a molecule that make it potentially suitable for development as a pharmaceutical drug, is crucial for determining whether a compound could be a viable drug candidate. Given the diversity and complexity of TCM, manually evaluating each compound for drug-likeness is impractical. Therefore, an efficient, systematic approach is needed to assess the drug-likeness of TCM compounds and understand the chemical structures that contribute to their therapeutic potential.

To address this challenge, this thesis proposes a data-driven approach using structured data and machine learning techniques to systematically evaluate the drug-likeness of TCM compounds, enabling the identification of promising candidates for pharmaceutical development