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bioRxiv · 10.1101/2023.01.11.523575

Variational graph encoders: a surprisingly effective generalist algorithm for holistic computer-aided drug design

Lam, H. Y. I.·Pincket, R.·Han, H.·Ong, X. E.·Wang, Z.·Li, W.·Hinks, J.·Zheng, L.·Wei, Y.·Mu, Y.
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Abstract

1.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 encoders 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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Lam, H. Y. I., Pincket, R., Han, H., Ong, X. E., Wang, Z., Li, W., Hinks, J., Zheng, L., Wei, Y., Mu, Y.. 2023-01-15. Variational graph encoders: a surprisingly effective generalist algorithm for holistic computer-aided drug design. https://doi.org/10.1101/2023.01.11.523575

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