arXiv AI
Jun 9
RetroReasoner: A Reasoning LLM for Strategic Retrosynthesis Prediction
arXiv:2603. 12666v2 Announce Type: replace-cross Abstract: Retrosynthesis prediction aims to identify reactants that can synthesize a given product molecule.
RetroDFM-R: Reasoning-Driven Retrosynthesis Prediction with Large Language Models via Reinforcement Learning
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arXiv AI
Jul 7
URSA: Chemistry-Aware Benchmark for Utilitarian Retrosynthesis Assessment
arXiv:2607. 04688v1 Announce Type: cross Abstract: Synthesis planning aiming to find pathways of reactions for a target molecule is one of the most important and challenging tasks in drug discovery.
arXiv AI
Jul 17
RetroAgent: Harnessing LLMs to Search Over Structured Memory for Agentic Retrosynthesis Planning
arXiv:2607. 14512v1 Announce Type: new Abstract: Multi-step retrosynthesis planning seeks to decompose a target molecule into commercially available building blocks through a sequence of feasible reactions.
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
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 AI
Aug 18
RetroMPA: A Molecular Property-Aware Auxiliary Framework for Enhancing Retrosynthesis Prediction
arXiv:2608. 16111v1 Announce Type: cross Abstract: Retrosynthesis is a cornerstone of drug discovery and organic 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.
arXiv AI
Sep 18
oMeBench: Towards Robust Benchmarking of LLMs in Organic Mechanism Elucidation and Reasoning
oMeBench is a large-scale, expert-curated benchmark designed to evaluate large language models (LLMs) on organic mechanism reasoning. It contains over 10,000 annotated mechanistic steps, including reaction type labels, intermediate structures, and difficulty ratings, and introduces the oMeS scoring framework to assess logical consistency and chemical structural similarity. Evaluation shows that while current LLMs display promising chemical intuition, they often fail to produce correct and consistent multi-step reasoning, though prompting and fine-tuning can bring smaller models up to the level of closed‑source frontier models.
arXiv Machine Learning
Sep 17
A Multitask Large Reasoning Model for Molecular Science
The paper introduces a multitask large reasoning model for molecular science that incorporates chemical knowledge via a multispecialist architecture, chain-of-thought supervision, and molecule-informed reinforcement learning. It coordinates prediction and inference specialists across ten molecular tasks—including description, generation, nomenclature translation, property prediction, and reaction prediction—using task-conditioned routing. The model surpasses more than 20 general-purpose and molecular large language models, improving aggregate performance by 50.3% and outperforming leading multitask baselines on most tasks, while maintaining interpretable chemical inference and demonstrating a workflow for CNS candidate generation and retrosynthetic planning.
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
Oct 15
RetroPlanner: Multi-Route Expansion and Multi-Objective Reasoning for LLM-Based Retrosynthesis Planning
arXiv:2610.16391v1 Announce Type: new Abstract: Multi-step retrosynthesis planning is often considered solved once a route to purchasable starting materials is found, but such a route should also be...
arXiv Machine Learning
Aug 12
Order Matters in Retrosynthesis: Structure-aware Generation via Reaction-Center-Guided Discrete Flow Matching
arXiv:2602. 13136v2 Announce Type: replace Abstract: Template-free retrosynthesis methods treat the task as black-box sequence generation, limiting learning efficiency, while semi-template approaches rely on rigid reaction libraries that constrain generalization.