1397-Generative AI for Efficient Large Molecule Design
This research explores the development of generative AI models specifically tailored for the design of large, complex molecules, which are often neglected in favor of small-molecule studies. The authors introduce a self-supervised learning framework and a novel tokenization method called Atom-Pair Encoding (APE) to overcome the computational inefficiencies and memory constraints of traditional graph-based models. By utilizing SELFIES and SMILES string representations, the study demonstrates that these enhancements significantly improve the validity, novelty, and scalability of generated…
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