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This paper proposes a Context-Augmented Prompting framework that uses a GNN expert model to provide predictive hints and explanatory subgraphs to improve molecular property prediction in small language models. Experiments on MUTAG and Tox21 show accuracy gains of up to 74% over SMILES-only baselines.
path_boost is a Python package implementing PathBoost, a gradient boosting algorithm for interpretable graph-level prediction. It automatically discovers predictive labeled paths in graphs, supports regression and binary classification, and is compatible with scikit-learn.
This paper reveals that standard marginal conformal prediction fails to cover minority classes in imbalanced virtual screening datasets, and demonstrates that class-conditional (Mondrian) conformal prediction restores per-class coverage.
This paper introduces a tri-branch modular fusion neural network that integrates 3D geometry, SMILES embeddings, and physicochemical descriptors for molecular property prediction, achieving a 20.6% error reduction on QM9 with fewer than one million parameters.
MOLAR proposes a noise-aware framework for learning multimodal molecular representations from noisy labels by separating clean-property inference from observed label noise, outperforming baselines on molecular benchmarks.
This paper introduces GLACIER, a multimodal student-teacher foundation model that integrates molecular graphs, SMILES strings, and physicochemical descriptors to predict molecular properties efficiently. It leverages Finsler geometry-aware fusion and knowledge distillation from larger teacher models (MiniMol, MolFormer) to achieve high performance with a lightweight architecture.