Researchers have developed MR-MoL, a novel molecular large language model (LLM) designed for property prediction in drug discovery. Unlike existing models that represent molecules implicitly, MR-MoL explicitly incorporates substructure attributions as a rationale. This rationale, derived from a fine-tuned graph neural network, guides the LLM by highlighting influential molecular components at various granularities, such as Murcko scaffolds, BRICS fragments, and functional groups. The model demonstrates superior performance on eight MoleculeNet tasks, outperforming generalist models and reducing the gap with specialized ones, while diagnostics confirm its effective use of the provided rationale. AI
IMPACT This approach could enhance the interpretability and accuracy of molecular property prediction, accelerating drug discovery by providing clearer insights into structure-property relationships.
RANK_REASON The cluster describes a novel method presented in an arXiv paper and covered by a daily paper digest, focusing on a new model architecture and its performance on benchmarks.
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