Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.01.11.523575v1?rss=1
Authors: Lam, H. Y. I., Pincket, R., Han, H., Ong, X. E., Wang, Z., Li, W., Hinks, J., Zheng, L., Wei, Y., Mu, Y.
Abstract:
While there has been significant 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 specialists 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 (ADMET), target-specific docking score prediction and drug-drug interactions. Considerably, the use of this method allows for state-of-the-art virtual screening with an acceleration advantage of up to two orders of magnitude. The minimisation 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.
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