arXiv AI
Jun 6
Agentic Molecular Recovery via Molecule-Aware Exploration
arXiv:2606. 05847v1 Announce Type: new Abstract: Text-guided molecular generation with LLMs often yields invalid SMILES.
MolGVR: A Chemistry-Grounded Framework for Text-to-Molecule Generation
arXiv:2607. 29479v1 Announce Type: new Abstract: Text-to-molecule generation is typically formulated as a one-shot sequence generation problem, where a model directly maps target descriptions to molecular representations.
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arXiv AI
Jun 3
From Answers to States: Verifiable Process-Level Evaluation of Chemical Reasoning in Large Language Models
arXiv:2606. 03660v1 Announce Type: new Abstract: Large language models are increasingly used as chemistry assistants, yet most chemistry benchmarks still score only final answers.
arXiv AI
Jun 3
Rethinking Molecular Text Representations for LLMs: An Empirical Study
arXiv:2606. 03057v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for molecular tasks, but it remains unclear which molecular representation to use.
arXiv AI
Jun 30
A Large-Scale Dataset for Molecular Structure-Language Description via a Rule-Regularized Method
arXiv:2602. 02320v4 Announce Type: replace-cross Abstract: Molecular function is largely determined by structure.
arXiv AI
Jun 2
When Single Answer Is Not Enough: Rethinking Single-Step Retrosynthesis Benchmarks for LLMs
arXiv:2602. 03554v2 Announce Type: replace-cross Abstract: Recent progress has expanded the use of large language models (LLMs) in drug discovery, including synthesis planning.
arXiv Machine Learning
Jul 2
MolSafeEval: A Benchmark for Uncovering Safety Risks in AI-Generated Molecules
arXiv:2607. 00464v1 Announce Type: new Abstract: Current molecular generation benchmarks emphasize task complexity, molecule novelty, and property alignment; they largely overlook a critical concern: the potential safety risks of AI-generated molecules.
arXiv Machine Learning
Jul 22
Adopting Reinforcement Learning with Verifiable Rewards for Molecular Generation
arXiv:2607. 19044v1 Announce Type: new Abstract: Leveraging large language models (LLMs) for molecular generation has shown remarkable potential in chemical and drug design.
arXiv AI
6d ago
Chemically Meaningful Textualization Enables Explainable Validation of Metal-Organic Frameworks by Large Language Models
arXiv:2608. 11283v1 Announce Type: cross Abstract: Computation-ready metal-organic framework (MOF) databases are essential for high-throughput screening, yet many reported crystal structures remain chemically unreasonable or disordered, compromising simulation fidelity.
arXiv Machine Learning
Jun 11
My Chemical Harness: Evolutionary Molecular Design over Synthetic Pathways with Large Language Model Agents
arXiv:2606. 11256v1 Announce Type: cross Abstract: Designing molecules with target properties is most useful when candidate structures are accompanied by feasible synthetic routes.
arXiv Machine Learning
Jun 5
MolE-RAG: Molecular Structure-Enhanced Retrieval-Augmented Generation for Chemistry
arXiv:2606. 05693v1 Announce Type: new Abstract: Large language models (LLMs) have shown promise for molecular property prediction, but their ability to reason over chemical structures remains limited, as molecular representations such as SMILES differ substantially from the natural language on which LLMs are primarily trained.
arXiv AI
Aug 10
Strategy-first synthesis planning for complex natural products
arXiv:2608. 07454v1 Announce Type: cross Abstract: The total synthesis of a complex molecule is among the most demanding intellectual and experimental feats in chemistry: a chemist must plan many steps ahead for how to assemble simple building blocks into an intricate target, devise backup strategies, and anticipate procedural challenges.
arXiv Machine Learning
Aug 4
SIGMA: Semantic Identifier Grouping for Molecular Autoregression
arXiv:2603. 25062v2 Announce Type: replace Abstract: Autoregressive molecular models assign probability to molecular serializations even though chemical identity is invariant to serialization.