De Novo Drug Property Prediction using Graph Convolutional Neural Networks

Drug property prediction poses a complex task in the healthcare domain, because the drug-like molecules greatly vary in chemical structures. Inhibitory concentration (IC
50
) prediction is essential to reduce the cost and labor of a drug's preclinical and clinical trials, because a lot of drug evaluations (cellular, animal, and clinical) can be spared due to the predicted IC
50
. In the era of Artificial Intelligence, Drug Discovery processes can benefit from deep learning, which has been widely used in computational chemistry and bioinformatics with state-of-the-art performance. In this paper, we propose a novel (in other words, de novo) graph convolutional network approach, cross-validated by traditional methods like Lipinski's rule of five and PaDEL-described one-hot encoding. The experiment has been carried out on the ChEMBL bioactivity dataset of Acetylcholinesterase protein, achieving an R2 score of 0.52.