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, Master of science program in 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