Generative AI for graph-based drug design: Recent advances and the way forward

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A2 Katsausartikkeli tieteellisessä aikakauslehdessä
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Date
2024
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Mcode
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Language
en
Pages
8
1-8
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Current Opinion in Structural Biology, Volume 84
Abstract
Discovering new promising molecule candidates that could translate into effective drugs is a key scientific pursuit. However, factors such as the vastness and discreteness of the molecular search space pose a formidable technical challenge in this quest. AI-driven generative models can effectively learn from data, and offer hope to streamline drug design. In this article, we review state of the art in generative models that operate on molecular graphs. We also shed light on some limitations of the existing methodology and sketch directions to harness the potential of AI for drug design tasks going forward.
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Garg , V 2024 , ' Generative AI for graph-based drug design: Recent advances and the way forward ' , Current Opinion in Structural Biology , vol. 84 , 102769 , pp. 1-8 . https://doi.org/10.1016/j.sbi.2023.102769