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 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 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.
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
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
Aug 26
Round-trip Reinforcement Learning: Self-Consistent Training for Better Chemical LLMs
The paper introduces Round-Trip Reinforcement Learning (RTRL), a framework that trains chemical language models to improve round‑trip consistency by rewarding successful forward and reverse transformations. By iteratively training forward and reverse mappings, RTRL leverages abundant unlabeled chemical data to enhance both consistency and overall performance across supervised, self‑supervised, and synthetic data regimes. Experiments show that RTRL outperforms strong baselines, demonstrating that round‑trip consistency can be treated as a trainable objective for more robust foundation models.