Retrosynthetic planning is a fundamental task in organic chemistry, yet remains challenging due to its combinatorial complexity. To address this, conventional approaches typically rely on hybrid frameworks that combine single-step predictions with external search heuristics, inevitably fracturing the logical coherence between local molecular transformations and global planning objectives. To bridge this gap and embed sophisticated strategic foresight directly into the model's chemical reasoning, we introduce ReTriP, an end-to-end generative framework that reformulates retrosynthesis as a direct Chain-of-Thought reasoning task. We establish a path-coherent molecular representation and employ a progressive training curriculum that transitions from reasoning distillation to reinforcement learning with verifiable rewards, effectively aligning stepwise generation with practical route utility. Empirical evaluation on RetroBench demonstrates that ReTriP achieves state-of-the-art performance, exhibiting superior robustness in long-horizon planning compared to hybrid baselines.
Figure 2: The ReTriP framework. (a) Path-coherent data construction: (a1) The target molecule undergoes notational augmentation. (a2) Iterative alignment ensures fragment traceability. (b) Progressive training curriculum: (b1) A three-stage SFT process transitions from trajectory modeling to reasoning distillation and loss-rebalancing calibration. (b2) RLVR aligns stepwise logic with precursor availability and synthetic efficiency. (c) Inference and scaling: A ranking-guided TTA selects optimal input notations, followed by a consensus-based voting mechanism.
Generative Model
Search Module
Top-1
Top-2
Top-3
Top-4
Top-5
Template-based
Retrosim ( Coley et al., 2017 )
Retro*
35.1
40.5
42.9
44.0
44.6
Neuralsym ( Segler and Waller, 2017 )
Retro*-0
42.0
49.3
52.0
53.6
54.3
GLN ( Dai et al., 2019 )
Retro*
39.6
48.9
52.7
54.6
55.7
Semi-template-based
G2Gs ( Shi et al., 2020 )
Retro*
5.4
8.3
9.9
10.9
11.7
Table 1: Exact match accuracy (%) on RetroBench. Baseline results are as reported by Liu et al. (2023b) , Liu et al. (2024b) , and Kang et al. (2025) .
Figure 4
Components
Accuracy (%) @ TTA Size
Path Alignment
CoT
RLVR
Pre-ranking
1
3
4
5
30.3
32.8
34.0
35.0
✓
33.5
36.1
37.6
38.5
✓
✓
33.9
37.3
39.2
40.6
✓
✓
✓
36.6
39.3
40.6
41.7
✓
✓
✓
✓
39.7
41.9
42.4
43.5
Table 2: Component-wise ablation study. Without pre-ranking, inference inputs are randomly sampled. Stochastic results report the mean of 10 runs.
Appendix figures & tables6 assets
Supplementary material from the paper’s appendix.
Appendix
Model Category
Model
Parameters
Single-pass Acc (%)
General LLMs
GLM-5
744B
1.8
DeepSeek-V3.2
671B
1.9
Qwen3.5-397B-A17B
397B
1.6
Kimi-K2.5
1.04T
1.0
Chemistry LLMs
ChemLLM-20B-Chat-DPO
20B
0.0
ChemDFM-R-14B
14B
0.5
Appendix
Table 3: Out-of-the-box performance of foundation models on RetroBench.
Figure 5: Qualitative case study. A generated 9-step retrosynthetic plan, featuring integrated CoT reasoning for critical steps.
Hyperparameter
Epochs
Batch Size
Learning Rate
LoRA Rank
Values
1.5
256
1e-4
16
Appendix
Table 4: Hyperparameters for each supervised fine-tuning stage.
Hyperparameter
Group Size
KL β
Batch Size
Learning Rate
Temperature
Top- p
Values
8
0.04
512
1e-6
1.0
0.95
Appendix
Table 5: Hyperparameters for the reinforcement learning stage.
Figure 6: Training reward curve of ReTriP during the RLVR phase. The evolution of the mean retrosynthetic reward demonstrates rapid convergence and effective objective alignment.
Figure 7: Trend of normalized Levenshtein distance (NLD) across retrosynthetic depths. The consistently lower NLD of our method demonstrates that fragment traceability is preserved throughout the planning horizon, minimizing representational noise.
Retrosynthesis prediction aims to identify reactants that can synthesize a given product molecule. Although molecular large language models (LLMs) have recently shown promising results, most existing methods either generate reactants directly or provide only generic product-level analysis, without explicitly reasoning about bond-disconnection strategies that justify specific reactant choices. This paper proposes RetroReasoner, a retrosynthetic reasoning model that captures chemists' strategic disconnection-based thinking. RetroReasoner is trained with supervised fine-tuning and reinforcement learning. For supervised fine-tuning, SyntheticRetro generates structured disconnection rationales paired with reactant predictions. For reinforcement learning, a round-trip reward evaluates predicted reactants by passing them through a forward synthesis model and rewarding predictions that reconstruct the original product. RetroReasoner can also be applied to multi-step retrosynthetic planning by incorporating it into a parallelized Monte Carlo tree search framework, reducing search time while increasing the number and diversity of valid synthetic pathways. Experimental results show that RetroReasoner outperforms prior baselines, including not only molecular LLMs but also retrosynthesis-specific expert models, and generates a broader range of feasible reactant proposals, especially for challenging reaction instances. The code is available at https://github.com/KU-AGI/RetroReasoner.
Hanbum Ko, Chanhui Lee, Ye Rin Kim +4
Department of Artificial Intelligence, Korea University · Materials Intelligence Lab, LG AI Research · Department of Statistics, Korea University
Multi-step retrosynthesis planning is often considered solved once a route to purchasable starting materials is found, but such a route should also be practical in real-world chemistry. Recent LLM-based planners formulate this process as sequential decision-making over an AND-OR graph, yet typically expand a single molecule per turn and are optimized for solvability alone, leaving alternative routes uncompared across the multiple objectives that determine practicality before the search budget is committed. We introduce RetroPlanner, an LLM-based planner that jointly compares multiple routes to steer the search toward higher synthesizability, as measured by reaction plausibility, round-trip consistency, and starting-material price. RetroPlanner is trained to reason over chemical evidence: structural progress from the target molecule, bond changes and reaction classes, where candidate routes diverge from one another, and how they rank across multiple objectives. Experiments on multi-step retrosynthesis benchmarks demonstrate that RetroPlanner recovers more alternative routes than prior LLM-based planners while improving synthetic route quality and maintaining a high success rate.
Ye Rin Kim, Yongjun Jeong, Seongjun Yun +4
Korea University, Department of Artificial Intelligence · Korea University, Department of Statistics
While LLMs show promise in general reasoning, symbolic planning in chemistry remains a bottleneck. Direct ''SMILES-to-PDDL'' attempts fail because they force models to juggle chemical analysis and planning-language structuring simultaneously. We hypothesize that this failure stems from a lack of intermediate abstractions rather than insufficient model capacity. By decomposing retrosynthesis into molecule mapping, reaction mapping, and PDDL generation, we achieve high success rates where end-to-end approaches fail. This provides evidence that a primary bottleneck lies in representation alignment rather than raw model capacity. Our structural analysis demonstrates that intermediate representations are essential in retrosynthesis planning, highlighting the importance of representation-centric design in future systems.