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We introduce G2T-LLM, a novel approach for molecule generation that uses graph-to-tree text encoding to transform graph-based molecular structures into a hierarchical text format optimized for large language models (LLMs).
Computer-based de novo design of drug-like molecules
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Deep learning for molecular design—a review of the state of the art
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Graph generation with diffusion mixture
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Molfm: A multimodal molecular foundation model
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Generating novel leads for drug discovery using llms with logical feedback
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Exploring the potential of large language models in graph generation
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