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:2603. 12666v2 Announce Type: replace-cross Abstract: Retrosynthesis prediction aims to identify reactants that can synthesize a given product molecule.
Retrosynthetic planning seeks to connect a target molecule to commercially available starting materials through a multistep route. Classical planners construct such routes by iteratively applying single-step reaction models within a search procedure; constrained variants often require specialized algorithms or architectural changes.
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:2606. 24184v1 Announce Type: new Abstract: Retrosynthetic planning seeks to connect a target molecule to commercially available starting materials through a multistep route.
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: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: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.
The paper presents an agentic retrobiosynthesis framework that uses a learned frontier selection policy to guide multistep retrosynthesis in a deterministic biochemical engine. By fine‑tuning a Qwen2.5‑7B policy with LoRA, the authors achieve higher solve rates than traditional MCTS across several benchmarks, demonstrating that route‑supervised frontier selection can improve search efficiency without changing the underlying reaction model.
arXiv:2606. 11256v1 Announce Type: cross Abstract: Designing molecules with target properties is most useful when candidate structures are accompanied by feasible synthetic routes.
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.
RetroGEF is a flow-based generative model designed for single-step retrosynthesis, which constructs possible reactants by adding atoms and altering bonds directly from a target molecule’s graph. Unlike traditional methods that rely on a fixed-size canvas or a prescribed edit order, RetroGEF models both connectivity changes and graph size variations within a single generative process, learning directly from product–reactant pairs. Experiments on standard retrosynthesis benchmarks show that RetroGEF achieves state‑of‑the‑art performance.