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 specifc 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 unifed model exploiting graph convolutional variational encoders that can simultaneously predict multiple properties such as absorption, distribution, metabolism, excretion and toxicity, target-specifc 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 specifc drugs for targets with Pareto optimality principles considered, and has the added advantage of explainability.
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