Starling, a multi-agent LLM system, extracts ~6.3 million nuanced structured records from PubMed across six tasks with reported error rates of 0.6-7.7%, lower than several curated databases.
$\texttt{MiniMol}$: A Parameter- Efficient Foundation Model for Molecular Learning, April 2024
4 Pith papers cite this work. Polarity classification is still indexing.
citation-role summary
citation-polarity summary
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cs.LG 4years
2026 4roles
method 1polarities
use method 1representative citing papers
Tabular foundation models achieve high accuracy in molecular property prediction through in-context learning, with up to 100% win rates on MoleculeACE tasks when paired with CheMeleon embeddings.
CAGenMol uses condition-aware discrete diffusion coupled with reinforcement learning to generate valid molecules meeting multiple heterogeneous constraints, outperforming prior methods on binding affinity, drug-likeness, and success rate benchmarks.
GLACIER combines graph, SMILES, and descriptor encoders with Finsler fusion and contrastive distillation to produce an efficient multimodal model for molecular property prediction.
citing papers explorer
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Self-Driving Datasets: From 20 Million Papers to Nuanced Biomedical Knowledge at Scale
Starling, a multi-agent LLM system, extracts ~6.3 million nuanced structured records from PubMed across six tasks with reported error rates of 0.6-7.7%, lower than several curated databases.
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Tabular foundation models for in-context prediction of molecular properties
Tabular foundation models achieve high accuracy in molecular property prediction through in-context learning, with up to 100% win rates on MoleculeACE tasks when paired with CheMeleon embeddings.
-
CAGenMol: Condition-Aware Diffusion Language Model for Goal-Directed Molecular Generation
CAGenMol uses condition-aware discrete diffusion coupled with reinforcement learning to generate valid molecules meeting multiple heterogeneous constraints, outperforming prior methods on binding affinity, drug-likeness, and success rate benchmarks.
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GLACIER: A Multimodal Student-Teacher Foundation Model for Molecular Property Prediction
GLACIER combines graph, SMILES, and descriptor encoders with Finsler fusion and contrastive distillation to produce an efficient multimodal model for molecular property prediction.