arXiv AI
oMeBench: Towards Robust Benchmarking of LLMs in Organic Mechanism Elucidation and Reasoning
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arXiv Machine Learning
Jul 15
Learning Mechanistic Reasoning for Chemical Reactions with Large Language Models
arXiv:2607. 12771v1 Announce Type: new Abstract: Reaction mechanisms consist of the step-by-step sequences of elementary reactions that explain chemical transformations.
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
Aug 25
RetroDFM-R: Reasoning-Driven Retrosynthesis Prediction with Large Language Models via Reinforcement Learning
arXiv:2507.17448v2 Announce Type: replace-cross Abstract: Retrosynthetic planning is a cornerstone of organic synthesis and drug discovery. Yet existing AI methods often rely on pattern matching rath...
arXiv Machine Learning
2d ago
A Multitask Large Reasoning Model for Molecular Science
arXiv:2603.12808v2 Announce Type: replace Abstract: Artificial intelligence in molecular science must move beyond pattern recognition toward chemically valid and interpretable reasoning. We present a...
arXiv AI
Jun 16
Retro-Expert: Collaborative Reasoning for Interpretable Retrosynthesis
arXiv:2508. 10967v3 Announce Type: replace-cross Abstract: Retrosynthesis prediction aims to infer the reactant molecules based on a given product molecule, which is a fundamental task in chemical synthesis.
arXiv AI
Aug 20
Training Chemical Plausibility-Aware Large Language Models for Single-Step Retrosynthesis
The paper introduces Top‑K prompting as a training and inference strategy to better capture the diverse, plausible predictions inherent in single‑step retrosynthesis. Using an ultra‑large dataset (CREED‑CCV‑2+USPTO‑XL) of ~45.6 million verified reactions, the authors train the Chemistry Constraint‑Consistent Language Model (C3LM). With fine‑tuning that incorporates ChemCensor‑based and novelty‑oriented rewards, C3LM achieves state‑of‑the‑art performance on the OOD URSA‑expert‑2026 benchmark and demonstrates complementary reaction space exploration compared to conventional models, suggesting benefits for ensemble‑based retrosynthesis systems.