Application of variational graph encoders as an effective generalist algorithm in computer-aided drug design

• Main Authors: Lam, Hilbert Yuen In, Pincket, Robbe, Han, Hao, Ong, Xing Er, Wang, Zechen, Hinks, Jamie, Wei, Yanjie, Li, Weifeng, Zheng, Liangzhen, Mu, Yuguang
• Other Authors: School of Biological Sciences
• Format: Article
• Language: English
• Published: 2023
• Subjects: Science::Biological sciences; Prediction; Database
• Online Access: https://hdl.handle.net/10356/171414
• Institution: Nanyang Technological University

Although there has been considerable progress in molecular property prediction in computer-aided drug design, there is a critical need to have fast and accurate models. Many of the currently available methods are mostly specialize in predicting specific properties, leading to the use of many models side-by-side that lead to impossibly high computational overheads for the common researcher. Henceforth, the authors propose a single, generalist unified model exploiting graph convolutional variational encoders that can simultaneously predict multiple properties such as absorption, distribution, metabolism, excretion and toxicity, target-specific docking score prediction, and drug–drug interactions. The use of such a method allows for state-of-the-art virtual screening with a considerable acceleration advantage of up to two orders of magnitude. The minimization of a graph variational encoder’s latent space also allows for accelerated development of specific drugs for targets with Pareto optimality principles considered, and has the added advantage of explainability.