MCLE-Mol: Empowering LLM with Molecular Comprehension and Low-Cost Continual Evolution for Interpretable Property Prediction
Year: 2026
Type: conference-paper
Abstract: Large language models (LLMs) offer a new paradigm for molecular property prediction (MPP), yet a semantic gap between natural language and molecular representations limits LLMs' ability to capture structure-activity relationships (SAR).Recent approaches have explored injecting structure-level information into LLMs, primarily by modeling associations based on statistical regularities.However, these methods are prone to misinterpreting coincidental associations as general principles, imposing a bottleneck on predictive performance.To tackle the challenges above, we propose MCLE-Mol, an ML-LLM-Rule collaborative framework for MPP.It bridges the semantic gap by injecting ML-derived substructure attribution values into LLMs, utilizing Context-Calibrated Substructure Attribution Rules (CCSAR) to calibrate these attributions under specific chemical contexts to mitigate such misinterpretation.In addition, MCLE-Mol introduces a low-cost continual evolution strategy that updates CCSAR with frozen model parameters to adapt to dynamic chemical spaces.Experiments on multiple benchmark datasets demonstrate that MCLE-Mol outperforms all baselines, successfully resolving the trade-off between predictive performance and interpretability.Our code is available at https: //github.com/DoorKickers/MCLE-Mol.
Institution: East China University of Science and Technology
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166806817