Semi-Supervised GCN for learning Molecular Structure-Activity Relationships

Autor:	Ragno, Alessio, Savoia, Dylan, Capobianco, Roberto
Rok vydání:	2022
Předmět:	Quantitative Biology - Biomolecules Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Since the introduction of artificial intelligence in medicinal chemistry, the necessity has emerged to analyse how molecular property variation is modulated by either single atoms or chemical groups. In this paper, we propose to train graph-to-graph neural network using semi-supervised learning for attributing structure-property relationships. As initial case studies we apply the method to solubility and molecular acidity while checking its consistency in comparison with known experimental chemical data. As final goal, our approach could represent a valuable tool to deal with problems such as activity cliffs, lead optimization and de-novo drug design. Comment: ELLIS Machine Learning for Molecules workshop (ML4Molecules) 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2202.05704 Zobrazit plný text záznamu View this record from Arxiv