Atom-anchored LLMs speak Chemistry: A Retrosynthesis Demonstration

## Abstract

Abstract Applications of machine learning in chemistry are often limited by the scarcity and expense of labeled data, restricting traditional supervised methods. In this work, we introduce a framework for molecular reasoning using general-purpose Large Language Models (LLMs) that operates without requiring labeled training data. Our method anchors chain-of-thought reasoning to the molecular structure by using unique atomic identifiers. First, the LLM performs a one-shot task to identify relevant fragments and their associated chemical labels or transformation classes. In an optional second step, this position-aware information is used in a few-shot task with provided class examples to predict the chemical transformation.
We apply our framework to single-step retrosynthesis, a task where LLMs have previously underperformed. Across academic benchmarks and expert-validated drug discovery molecules, our work enables LLMs to achieve high success rates in identifying chemically plausible reaction sites ( ≥ 90 % \geq 90\% ), named reaction classes ( ≥ 40 % \geq 40\% ), and final reactants ( ≥ 74 % \geq 74\% ). Beyond solving complex chemical tasks, our work also provides a method to generate theoretically grounded synthetic datasets by mapping chemical knowledge onto the molecular structure and thereby addressing data scarcity.

## 1 Introduction

General-purpose large language models (LLMs) have advanced rapidly in recent years, finding increasing application in the domain of chemistry. A prominent example of this trend is the use of LLMs like GPT-4 Achiam et al. (2023) as high-level reasoning agents that leverage specialized chemistry tools to automate complex tasks Boiko et al. (2023); M. Bran et al. (2024). In this paradigm, the LLM orchestrates tool calls that encapsulate chemical logic and subsequently reasons over the tool outputs.

Beyond the use of general-purpose models, prevailing approaches either train specialized chemistry LLMs or adapt general-purpose LLMs to the chemical domain, where molecular data is represented in the Simplified Molecular Input Line Entry System (SMILES) format Weininger (1988); Weininger et al. (1989), a chemical notation for representing chemical graph structures as computer-readable strings. Examples of specialized chemistry LLMs include models that are solely pre-trained on SMILES data and then either fine-tuned for a specific downstream task (e.g., Ross et al. (2022); Irwin et al. (2022)) or used to extract molecular embeddings for downstream tasks (e.g., Ross et al. (2022); Sadeghi et al. (2024); Masood et al. (2025)).
Alternatively, general-purpose LLMs are adapted to the chemical domain through methods such as supervised fine-tuning (SFT) Kim et al. (2024); Cavanagh et al. (2024), preference optimization (PO) Cavanagh et al. (2024), or the direct extraction of task-specific embeddings from general-purpose LLMs Sadeghi et al. (2024).
Finally, recent work adapts Chain-of-Thought (CoT) Wei et al. (2023) chemistry reasoning models following the Deepseek-R1 Guo et al. (2025) paradigm, e.g., ether0 Narayanan et al. (2025) fine-tunes Mistral-Small-24B-Instruct Mis using SFT on Deepseek-R1 reasoning traces and PO on chemistry tasks.

However, a central challenge in chemical machine learning is the scarcity and high cost of labeled data. This presents a significant limitation, as the aforementioned approaches all rely on labeled data for model training. Nevertheless, recent studies have shown that general-purpose LLMs are capable of reasoning over chemical structures, yet this capability is often exercised indirectly.
For instance, general-purpose LLMs have been used to enrich SMILES with text descriptions to fine-tune smaller models Qian et al. (2023), address diverse chemistry tasks via zero-shot and few-shot prompting with varying success Guo et al. (2023), and solve chemical mathematical calculations by generating and refining code-based solutions Ouyang et al. (2024).
A final category of applications addresses synthesis planning, the task of identifying viable synthetic routes by deconstructing a target molecule into smaller precursors using reactions until a set of commercially available starting materials is found Segler et al. (2018); Corey and Cheng (1989).
In this context, LLMs can reason about chemical structures to guide and evaluate the synthesis planning process itself based on a desired provided route outcome prompt, without directly manipulating the structures Bran et al. (2025).
As LLMs tend to struggle with generating high-quality reaction predictions directly, they can be paired with an evolutionary algorithm to reason over and evolve a population of full synthesis routes Wang et al. (2025). To ensure chemical validity, this process uses a database of known reactions and molecule routes, which are queried via a nearest-neighbor search in an embedding space to identify structurally similar precedents for chemical grounding.

In this work, we build on these insights to introduce a framework that enables general-purpose LLMs to successfully reason directly over molecular structures. Our method works by anchoring the reasoning process to a molecule’s atom-maps, which are unique identifiers for each atom in a molecular SMILES. This approach mirrors a chemist’s workflow, operates without labeled training data or task-specific model training, and consists of two stages. First, in a zero-shot task, the model performs a chemical analysis on the chemical structure to identify the atom-maps of relevant fragments for the task and assigns structural labels for these fragments solely based on chemical reasoning. Second, in an optional few-shot task, it transforms the chemical structure based on these identified fragments, guided by examples from a specific chemical transformation class (e.g., a particular reaction or other defined chemical transformation).

We apply this framework to single-step retrosynthesis, where the goal is to identify, given a product molecule, a set of plausible reactant molecules (precursors) that can form the product in a single reaction step Torren-Peraire et al. (2024). Formally, the goal is to learn a function $f(P)\rightarrow[R_{1},R_{2},\dots,R_{n}]$ that maps a product molecule $P$ to a ranked list of plausible reactant sets, $[R_{1},R_{2},\dots,R_{n}]$, where each $R_{i}$ is a set of one or more reactant molecules, $\{r_{1},r_{2},\dots\}$, proposed to synthesize $P$.
In this task, prior research shows that general-purpose LLMs are not competitive with specialized models as they underperform their specialized counterparts by more than 40 percentage points in top-1 accuracy Guo et al. (2023) or solve only one out of five test examples correctly Li et al. (2025).
Our approach marks a shift from conventional supervised methods, which either (1) directly map products to reactants using Transformers Irwin et al. (2022); Tetko et al. (2020), Graph Neural Networks Chen and Jung (2021); Zhong et al. (2023), Markov Bridges Igashov et al. (2024), or fine-tuned LLMs Yang et al. (2024); Nguyen-Van et al. (2024), or (2) use a two-step, disconnection-aware paradigm where a model first learns to identify a bond disconnection site and second applies a transformation afterward.
Our approach evolves the second paradigm. Whereas these supervised methods apply a learned mapping by selecting a site either automatically Thakkar et al. (2023); Kreutter and
Reymond (2023) or with human guidance Thakkar et al. (2023); Westerlund et al. (2025), our work introduces explicit chemical reasoning as the core mechanism for both steps, leading to the following key contributions:

- 1.
We introduce a novel reasoning framework that enables LLMs to zero-shot analyze and few-shot transform molecular structures without task-specific training by anchoring their reasoning process directly to the molecule’s SMILES atom maps, thereby eliminating the need for labeled training data or task-specific model training.
- 2.
We demonstrate the framework’s effectiveness in single-step retrosynthesis on both academic benchmarks and expert-validated real drug discovery molecules, where it successfully identifies strategic disconnections, executes the corresponding transformation to predict reactant structures, and provides a chemically-grounded, explainable rationale for its predictions.
- 3.
We establish a general blueprint for applying LLMs to data-scarce problems in computational chemistry, enabling the generation of novel synthetic datasets by mapping general chemical knowledge directly onto molecular structures.

## 2 Methods

Figure: Figure 1: Adaptation of our general framework to the task of retrosynthesis. First, the Zero-Shot Position Model ($f_{\text{position_retro}}$ or $f_{\text{p_retro}}$, guided by $r_{\text{position}}$) analyzes an atom-mapped product $m_{0}$ together with the reaction ontology $O$ to identify and rank disconnection candidates $(S_{i},\beta_{i},\iota_{i},\rho_{i})$. Second, the (optional) Few-Shot Transition Model ($f_{\text{transition_retro}}$ or $f_{\text{p_retro}}$, guided by $r_{\text{transition}}$ and a library $L_{\text{retro}}$ of $\beta_{i}$ reaction examples) applies the selected reaction $\beta_{i}$ at the site $S_{i}$ to generate plausible reactant molecules ($R_{k}$) with validity assessment ($\gamma_{k}$) and chemical rationale ($\omega_{k}$).
Refer to caption: x1.png

### 2.1 Framework

Conventional drug discovery models learn a direct mapping $f:\mathcal{X}\rightarrow\mathcal{Y}$, treating molecular representations $x\in\mathcal{X}$ as abstract data points to predict properties $y\in\mathcal{Y}$. This paradigm disregards the underlying chemical knowledge that could govern the relationship $r$ between a molecule’s structure and its properties. In contrast, our approach circumvents this data-driven mapping by leveraging the emergent reasoning capabilities of a pre-trained LLM. Guided by a natural language prompt, the LLM performs a detailed chemical analysis with its reasoning explicitly anchored to the molecule’s SMILES atom maps, ensuring a precise linkage to specific structural locations. This structurally-grounded analysis enables the direct inference of chemical properties, eliminating the need for task-specific fine-tuning. Our approach operates in two stages:

- 1.
Zero-Shot Structural Analysis and Property Prediction (Position Model): Guided by a natural language prompt $r_{position}$ that encodes domain knowledge about the task, the LLM analyzes an atom-mapped molecule SMILES $m$ to identify relevant substructures. Based on this prompt-guided reasoning, which is explicitly linked to atom map indices, the position model $f_{position}(m)$ predicts a set of properties $P=\{p_{1},\dots,p_{n}\}$. Each prediction $p_{i}$ is a tuple $p_{i}=(S_{i},A_{i})$, where $S_{i}\subseteq V(m)$ is a set of atom indices from the molecule $m$ (the structural label), and $A_{i}=(a_{1},a_{2},...,a_{k})$ is an ordered tuple of inferred chemical attributes relevant to the task (e.g., "toxic," "reaction"). Each individual attribute $a_{i}$ in this tuple can be a passive descriptor or an actionable transformation.
- 2.
Prompt-Guided Molecular Transformation (Transition Model): In an optional second phase, predictions $p_{i}=(S_{i},A_{i})$ containing an actionable transformation in their attribute tuple $A_{i}$ are executed.
For each general chemical task, a transformation function $f_{transition}$ is defined by a second natural language prompt $r_{transition}$. This transition function executes an actionable attribute $a_{j}\in A_{i}$ by applying $f_{transition}$ to an initial molecule $m_{0}$ at the location $S_{i}$ to yield a new molecule $m_{1}$, such that $m_{1}=f_{transition}(m_{0},S_{i},a_{j},L)$.
Here, $L$ is a context library providing examples or any relevant information for the established chemical operations identified by the actionable attribute $a_{i}$ from the tuple $A_{i}$. This is feasible because many chemical transformations are discrete, well-established operations, allowing in-context learning to ensure chemical validity.

### 2.2 A Position Model for Retrosynthesis

The Position Model emulates a human chemist’s analytical workflow to identify and rank potential disconnection sites in a product molecule. Formally, given an atom-mapped product molecule $m$, the Position Model is a function $f_{position_retro}(m)$ that predicts a set of potential retrosynthetic disconnection candidates, $D=\{d_{1},d_{2},\dots,d_{N}\}$. Each candidate $d_{i}=(S_{i},\beta_{i},\iota_{i},\rho_{i})$, which instantiates the general property prediction $p_{i}=(S_{i},A_{i})$ for retrosynthesis, is generated by the function:

$$ $D=\{(S_{i},\beta_{i},\iota_{i},\rho_{i})\}_{i=1}^{N}=f_{position_retro}(m_{0},O)$ $$

This function maps a set of inputs:

- •
$m_{0}$: The atom-mapped target product molecule canonicalized SMILES.
- •
$O$: A reaction ontology containing reaction names corresponding to a library of executable transformations $L$, providing a bridge to the optional transformation phase.

to a set of $N$ distinct tuples:

- •
$S_{i}\subseteq V(m)$ is the structural label: a set of atom indices defining the disconnection point.
- •
$\beta_{i}$ is the predicted reaction name: a chemical attribute identifying a suitable transformation (e.g., "Suzuki Coupling"). To make this actionable, we ground predictions using the reaction ontology ($O$), but do not strictly constrain them, allowing the suggestion of reactions outside of $O$ (which are flagged).
- •
$\iota_{i}\in\mathbb{R}$ is the retrosynthesis importance: a score ranking the strategic value of the disconnection, which can be used to prioritize the most promising reactions (e.g., major ring-forming reactions, core scaffold construction).
- •
$\rho_{i}$ is the chemical rationale: a text-based justification tied to primary strategic goals of retrosynthesis (e.g., structural simplification, reaction robustness, and stereochemical control).

The entire reasoning process of $f_{position_retro}$ is defined by a natural language prompt $r_{position}$ (see Prompt [1](https://arxiv.org/html/2510.16590v1#LST1)). Crucially, $r_{position}$ does not contain explicit transformation rules (e.g., SMARTS patterns) or any other reaction-specific rules. Instead, it instructs the LLM to emulate a chemist’s analytical workflow.
Reframing the retrosynthesis task necessitates a shift in evaluation, moving beyond classical top-n performance based on product-reactant replication. Our evaluation instead measures the model’s ability to correctly identify the ground-truth disconnection site and reaction type, for which the following metrics are defined:

- 1.
Partial Match Accuracy: An indicator metric that is true if any predicted disconnection $S_{i}\in D$ has a non-empty intersection with the ground truth $S_{gt}$.
- 2.
Best Match Jaccard: The highest Jaccard similarity between any predicted structural label $S_{i}\in D$ and the ground truth set $S_{gt}$.
- 3.
Exact Match Accuracy: A stricter metric that is true if the best-matching predicted disconnection site (by Jaccard score) is identical to the ground truth $S_{gt}$.
- 4.
Conditional Reaction Accuracy: Conditional on a partial match and the highest Jaccard similarity in $D$, this metric evaluates the reaction name(s) $\beta_{i}$ from the disconnection candidate(s) $d_{i}$. The metric is 1 if any of these $\beta_{i}$ match the ground truth reaction name, $\beta_{gt}$.

### 2.3 A Transition Model for Retrosynthesis

To complete the retrosynthesis workflow, we define the Transition Model as $f_{\text{transition_retro}}$. This model uses a disconnection candidate $d_{i}$ and a target product $m_{0}$ to generate a set of plausible reactants $R$. To simulate a chemist’s literature lookup for a reaction, the reaction name $\beta_{i}\in O$ is used to sample up to five reaction examples from a training dataset to create the task-specific, in-context library $L_{\text{retro}}$. The one-to-many Transition Model is then defined as:

$$ $\{(R_{k},\gamma_{k},\omega_{k})\}_{k=1}^{N}=f_{transition_retro}(m_{0},S_{i},\beta_{i},L_{\text{retro}})$ $$

This function maps a single set of inputs:

- •
$m_{0}$: The atom-mapped target product molecule canoncialized SMILES.
- •
$S_{i}$: The set of disconnection point atom indices.
- •
$\beta_{i}$: The reaction name, serving as the actionable attribute $a_{j}$.
- •
$L_{\text{retro}}$: The context library, containing examples of the reaction $\beta_{i}$.

to a set of $N$ distinct tuples:

- •
$R_{k}$: The $k$-th predicted set of reactant molecules $\{r_{1},r_{2},\dots,r_{n}\}$.
- •
$\gamma_{k}$ : The specific chemical validity assessment (stability, chemoselectivity, stereochemical consistency) for the transformation leading to $R_{k}$.
- •
$\omega_{k}$: The specific chemical rationale that justifies the validity of the $k$-th outcome.

The transition function $f_{transition_retro}$ is defined by prompt $r_{transition}$ (see Prompt [2](https://arxiv.org/html/2510.16590v1#LST2)), which emulates a chemist’s reasoning and avoids explicit reaction rules. Beyond reactant prediction, the model can also generalize transformations by abstracting a reaction template $R_{t}$, which is flagged accordingly. This template can handle complex cases, such as multiple atoms being viable for reaction side or added reagents, thereby preventing exhaustive iteration. We evaluate performance by comparing the predicted reactant sets, $R_{\text{pred}}=\{R_{1},\dots,R_{N}\}$, against the ground-truth reactants, $R_{\text{gt}}$. As multiple reactant sets can be chemically valid, our goal is to assess the model’s ability to recover the known, ground-truth transformation without ranking. The following metrics are calculated per-prediction and averaged across the dataset.

- 1.
Template Accuracy: measures if any predicted reactant template set $R_{t}\in R_{\text{pred}}$ correctly identifies the core structure of the ground-truth reactants $R_{gt}$. A prediction is considered a match if for every ground-truth reactant $r_{gt}\in R_{gt}$ there is a corresponding predicted reactant template $r_{t}\in R_{t}$ sharing at least 75% of its atoms and having a direct substructure match.
- 2.
Reactant Accuracy: measures if any predicted reactant set $R_{k}$ is an exact, non-template match for the ground-truth set $R_{gt}$.
- 3.
Combined Accuracy: measures if a prediction meets either the Template or Reactant Accuracy criterion.

### 2.4 Experimental Setup

We evaluate the Position ($f_{position_retro}$) and Transition ($f_{transition_retro}$) models across a diverse set of LLMs to assess the scaling of reasoning capabilities. Our selection includes various open-source models (Qwen3-2507 4B, 30B, 235B Yang et al. (2025), DeepSeek-R1-0528 Guo et al. (2025)), several closed-source models (Gemini 2.5 Flash/Pro Comanici et al. (2025), Claude Sonnet 4 Anthropic (2025), GPT5 OpenAI (2025)), and a chemistry-specialized model, ether0 Narayanan et al. (2025). For efficiency, the largest open-source models were quantized for inference on an 8x H100 DGX node and used default inference parameters (see Table [2](https://arxiv.org/html/2510.16590v1#S6.T2)).

We use two public reaction datasets: USPTO50k Lowe (2012); Schneider et al. (2016) and PaRoutes Genheden and Bjerrum (2022). For USPTO50k ($n\approx 5\times 10^{4}$), we use an adjusted version that corrects a known atom-mapping bias Somnath et al. (2021). For PaRoutes ($n\approx 1\times 10^{6}$), we use the provided data splits Torren-Peraire et al. (2024).
For all datasets, we preprocess the data to generate structural labels ($S_{i}$), reaction names ($\beta_{i}$) and reaction ontology ($O$). The labels ($S_{i}$) define the reaction center by annotating atoms of bonds that are broken, formed, or changed in type from the product’s perspective. We prioritize changes in connectivity (bonds breaking or forming) over bond type changes, where the atom structure itself remains unchanged, unless no connectivity change occurs. The reaction names ($\beta_{i}$) and their reaction classes are extracted using the open-source rxn-insight package Dobbelaere et al. (2024), allowing the release of our labeled data.
The ontology ($O$) is constructed from unique reaction names ($\beta_{i}$) in the respective training data.
To mitigate the skewed distribution of reaction names in the USPTO50k test set ($n=5\times 10^{3}$) and prevent redundant evaluation, we create a subsampled version, USPTO50k-LLM (see Fig. [5](https://arxiv.org/html/2510.16590v1#S6.F5)). This 541-point evaluation set contains up to five examples per unique reaction name, preserving the original proportion of unclassified reactions. Unless specified otherwise, we use this set with a reaction ontology ($n=136$) derived from the USPTO50k training data.

## 3 Results

### 3.1 Position Model

Our analysis of structural chemical reasoning shows performance scales with model size, with large closed-source models such as the top-performing Gemini 2.5 Pro required for the best results (Fig. [2](https://arxiv.org/html/2510.16590v1#S3.F2)). We evaluated models on four tasks of increasing difficulty: partial position match, maximizing Jaccard overlap, exact position match, and correct reaction prediction given a partial match. A consistent pattern emerged, where performance increased with the size of the model. For instance, partial match scores jumped from 73% for 4B models to 87% for 235B+ models. This trend held across all tasks, with the performance gap becoming most stark on the reaction prediction task, where smaller models scored just 4%. In contrast, only the largest proprietary models achieved a moderate success rate of $40\text{-}47\%$, showing a trade-off between higher accuracy and lower prediction efficiency (i.e., more predictions per success; see Table [3](https://arxiv.org/html/2510.16590v1#S6.T3)).

Figure: Figure 2: A) Position model performance on USPTO-LLM. The plot compares various foundation models on the task of reaction position prediction, measured by four evaluation metrics: achieving a partial positional match, maximizing the Jaccard metric, identifying the exact position, and predicting the correct reaction (conditional on a partial match). B) Confusion matrix of predicted versus ground-truth reaction classes for the Gemini 2.5 Pro model on USPTO-LLM. The analysis is conditional, including only predictions where the model successfully identified at least a partial positional match. For this visualization, reactions outside the defined reaction ontology were excluded. The matrix was generated using the original class-to-name mappings from the ground-truth data, with any unassigned reactions grouped into the ’Miscellaneous’ category.
Refer to caption: x2.png

Three models warrant a specific discussion. First, the ether0 model, a Mistral-24B variant fine-tuned for chemistry, fails to produce any valid predictions, generating neither valid outputs nor chemically valid positions, unlike other models that fail only occasionally (see Table [3](https://arxiv.org/html/2510.16590v1#S6.T3)). This total failure suggests that its specialized training, which utilizes chemistry reasoning traces and GRPO on chemical tasks, hindered generalizability to our problem. Second, an ablation of Qwen-235B-Instruct reveals a trade-off with its thinking counterpart. Despite a comparable partial match score, the instruct model showed poor prediction efficiency, generating far more candidate positions, and was only half as effective at identifying the correct reaction (see Table [3](https://arxiv.org/html/2510.16590v1#S6.T3)), highlighting the importance of CoT reasoning. Interestingly, this pattern does not appear for Gemini 2.5 Flash, where its thinking and non-thinking versions perform comparably with high reaction accuracy and low prediction efficiency.

Our problem involves a one-to-many relationship in which a chemical position can have multiple valid reactions. To evaluate one of the best performing models, Gemini 2.5 Pro, we mapped its predictions to broader reaction classes using the reaction class mapping from rxn-insight on the ground truth data (see Fig. [2](https://arxiv.org/html/2510.16590v1#S3.F2)). The model often suggests alternative reactions from the correct class rather than predicting a reaction from a different class. However, some exceptions represented chemically plausible alternative strategies: for ’Aromatic Heterocycle Formation’, the model often predicted ’C-C couplings’, and for ’Protection’ reactions, it suggested ’Reductions’. The ’Heteroatom Alkylation and Arylation’ class was a notable outlier, being proposed for most other categories except ’FGI’ and ’C-C couplings’. This predictive pattern of staying within-class and these specific exceptions also holds at the individual reaction-name level (see Fig. [6](https://arxiv.org/html/2510.16590v1#S6.F6)).

### 3.2 Transition Model

We evaluated various LLMs on their ability to predict ground-truth transformations using the reaction’s position, name, and up to five examples (Figure [3](https://arxiv.org/html/2510.16590v1#S3.F3)). Model performance scales logarithmically with size before plateauing at the scale of Deepseek-R1. Gemini 2.5 Pro is the top performer, excelling both at direct reactant prediction ("Reactant"; see example Fig. [11](https://arxiv.org/html/2510.16590v1#S6.F11)) and in combination with a reaction template ("Combined"). This template generation ("Template"; see example Fig. [12](https://arxiv.org/html/2510.16590v1#S6.F12)), which is a proxy for chemical understanding, is strongest in proprietary models, such as GPT-5 and Gemini 2.5 Pro ($44\%$ accuracy). In contrast, Deepseek-R1 performs worse than its smaller open-source peers in template prediction, while ether0 fails again at this task.

Figure: Figure 3: Transition Model Performance on USPTO-LLM. The plot evaluates various LLMs on their ability to predict chemical transformations. Accuracy is measured using three metrics: direct reactant prediction (’Reactants’), valid template generation (’Template’), and a combined approach where either is considered a success (’Combined’).
Refer to caption: x3.png

In a first ablation study, our results reveal the critical importance of a defined reaction name to act as a chemical anchor (Appendix Fig. [7](https://arxiv.org/html/2510.16590v1#S6.F7)). Performance dropped by approx. $50\%$ for unknown reactions in a zero-shot setting (no examples provided) compared to known ones in a few-shot setting (up to five examples). The decline was particularly severe for the prediction of direct reactants, with accuracy falling from approximately $75\%$ to $30\%$. In a second ablation study on Gemini 2.5 Pro, we further isolate the contributions of prompt detail versus few-shot examples on overall ("combined") performance (Appendix Figure [8](https://arxiv.org/html/2510.16590v1#S6.F8)). Although the model achieved (52%) baseline accuracy from a minimal prompt, and the detailed prompts offered some improvement through the reaction template (59%), the inclusion of examples was the dominant factor (69%); a simple prompt with examples was much more effective than a detailed prompt without them. The best performance required both (81%). Finally, CoT reasoning improves reactant and combined accuracy, but it underperforms non-reasoning models on reaction template prediction, at the cost of lower prediction efficiency (compare Qwen3-235B Table [4](https://arxiv.org/html/2510.16590v1#S6.T4)).

Analyzing LLM failure modes reveals two distinct error types (Appendix Fig. [9](https://arxiv.org/html/2510.16590v1#S6.F9)). First, reaction class-specific performance variations among the top-performing models indicate that no single model is universally superior, suggesting solutions such as multi-model ensembles or best-of-n sampling. Second, all models consistently fail on a small set of reaction classes with few data points (e.g., Wohl-Ziegler bromination). This systemic failure likely stems from data deficiencies, such as incorrect labeling, poor examples that make the task ill-posed, rather than fundamental mechanistic reasoning challenges for current LLM architectures.

## 4 Application

Figure: Figure 4: Zero-shot position model prediction for compound LEI-515 Jiang et al. (2023) using the PaRoutes reaction ontology highlighting reaction priority 1. See Table LABEL:table:appendix_disconnections-lei-515 for all priorities (1-14).
Refer to caption: x4.png

While LLMs demonstrated strong performance on USPTO50k, such academic tests risk data contamination for models pre-trained on vast data corpora. To conduct a more rigorous, real-world validation, we evaluated our approach on five molecules that were previously synthesized and published in high-impact journals (see Fig. [10](https://arxiv.org/html/2510.16590v1#S6.F10)), for which we were able to discuss the experimental procedures with the respective lab chemists. Although this small sample size prevents broad statistical generalization, the case study provides a crucial assessment of the model’s practical capabilities and limitations.
For this evaluation, we used one of our top-performing LLMs (Gemini 2.5 Pro) with the PaRoutes reaction ontology (n=335) and annotated atom-maps by sequentially counting the atoms in a canonicalized SMILES. Our position model first proposed potential disconnection points, which the respective lab chemist of the molecule then curated for chemical relevance and to avoid redundancy for the transition model evaluation (an example for LEI-515 is provided in Appendix LABEL:table:appendix_disconnections-lei-515). This process yielded 63 distinct position predictions for assessment and 19 selected positions with a total of 98 transitions. Afterwards, the chemist assessed these predictions against predefined questions, and we calculated accuracy as the percentage of correct model responses (compare Table [1](https://arxiv.org/html/2510.16590v1#S4.T1)).

**Table 1: Questions for chemists with regard to the Position model (P) and Transition Model (T). n indicates here the overall number of data points and accuracy (Acc.), as well as the percentage of correct predictions. Actionable refers here to non-template and not to chemically invalid predicted reactant sets from the model. We provide a full overview in the appendix (see Table [6](https://arxiv.org/html/2510.16590v1#S6.T6) & [7](https://arxiv.org/html/2510.16590v1#S6.T7))**
| Question | n | Acc. |
| --- | --- | --- |
| P1: Disconnection position chemically plausible? | 63 | 90.5 |
| P2: Reaction correct for the proposed disconnection position? | 63 | 85.7 |
| P3: Chemical reasoning correct for the position and reaction? | 63 | 73.0 |
| P4: Given all the information, could this reaction realistically work in the lab? | 63 | 77.8 |
| P5: Specific reaction successfully performed in the lab for the molecule? | 63 | 25.4 |
| P6: Strategically important disconnection predictions missing for the molecule? | 5 | 80.0 |
| T1: Given a predicted reaction template, does it capture the underlying reaction? | 16 | 81.3 |
| T2: Given a predicted reaction template, is the chemical reasoning correct? | 16 | 87.5 |
| T3: Among the reactant predictions, is there at least one chemically correct set? | 19 | 89.5 |
| T4: Given the correct set of reactants, is the chemical reasoning also correct? | 19 | 89.5 |
| T5: Given the reaction was used in the lab, are the predicted reactants the same? | 15 | 73.3 |
| T6: Given that the reactants are flagged ’chemically invalid’, is the reasoning correct? | 7 | 100 |
| T7: What % of all the actionable suggested reactants are chemically correct? | 98 | 74.5 |

The case study results were highly encouraging. The model’s suggested disconnection points (90.5%) and associated reaction names (85.7%) were overwhelmingly judged as chemically plausible, with the latter often providing non-obvious alternatives to our expert chemists. While the accuracy for chemical reasoning was lower (73.0%), a majority of all suggestions (77.8%) were deemed applicable in a laboratory setting. Notably, the model rediscovered 25.4% of the experimentally validated disconnections. This figure is lower because the model often proposes multiple valid reactions for a single position, where only one would be used in practice.
However, the system has limitations. For four of the five molecules evaluated, the model missed disconnections anticipated by our chemists. It might, for example, propose a feasible reaction (e.g., Buchwald-Hartwig coupling) where an expert would prefer an alternative (e.g., an SNAr reaction). Our analysis indicates that errors typically originate from the LLM’s misinterpretation of the molecular structure (compare, for instance, the misidentified Cl position in Table LABEL:table:appendix_disconnections-lei-515, position 10). This initial error then propagates through the prediction, ultimately leading to an incorrect suggestion for the position, reaction or reasoning.

A key strength of the position model is its ability to provide a comprehensive set of plausible disconnections for an entire synthetic route, not just a single retrosynthetic step. Our chemists considered these predictions valid if the proposed disconnection could occur at any stage of the synthesis route. This holistic approach has two important consequences: First, the generated positions constrain the search space for a synthesis planning algorithm (e.g., Hassen et al. (2025)), streamlining the identification of an optimal reaction sequence. Second, these predictions highlight vectors for molecular modification, proving invaluable for guiding and accelerating medicinal chemistry campaigns by providing a strategic blueprint for replacing molecular cores or side-chains.

The transition model also demonstrated strong performance. It achieved 81.3% accuracy for predicting reaction templates and 87.5% for the associated reasoning, although chemists noted it worked mainly for standard reactions and is less reliable for complex ones (see Fig. [12](https://arxiv.org/html/2510.16590v1#S6.F12)). In 89.5% of cases, the model generated at least one chemically valid reactant set with sound reasoning (see Fig. [11](https://arxiv.org/html/2510.16590v1#S6.F11)), a reasoning quality judged comparable to that of a master’s or PhD-level chemist. Furthermore, it successfully identified 73.3% of reactants previously conducted in the lab. A key strength was its perfect (100%) accuracy in identifying non-viable reactions (see Fig. [13](https://arxiv.org/html/2510.16590v1#S6.F13)), correctly explaining why a proposed reaction would fail (e.g., identifying that a specific atom cannot exist at a given position). This highlights its role as a filter, as it sometimes corrected position model suggestions by proposing more intuitive reactions or filtering out disconnections that were invalid without prerequisite synthesis steps.
The model achieved a 74.5% overall accuracy in predicting reactants after excluding predictions that were reaction template-based or flagged as chemically invalid. Failures typically occurred in one of two ways: the model either failed to return any valid reactant set (accounting for 15/29 failures in our evaluation), or it failed due to incorrect SMILES parsing (see Fig. [14](https://arxiv.org/html/2510.16590v1#S6.F14)), even when the underlying chemical reasoning was correct.

## 5 Conclusion

We introduce a reasoning framework that leverages the chemical knowledge in general-purpose LLMs to address data scarcity in computational chemistry, requiring no labeled training data or task-specific model training.
Our framework anchors chain-of-thought reasoning to the molecular structure by using atom maps in molecular SMILES as chemical anchors and operates in two stages: a one-shot position model identifies relevant molecular fragments and their associated chemical labels or transformations, and an optional position-aware few-shot transition model executes chemical transformations based on provided class examples. Applied to single-step retrosynthesis without task-specific training, our method effectively identifies chemically valid and strategically sound disconnection positions, their corresponding reaction classes, and reactant structures for both academic and expert-validated real-world drug molecules, while providing a chemically grounded, explainable rationale for each prediction.
Our work immediately enables new approaches in chemistry, where identified disconnection points define the search space for synthesis planning Westerlund et al. (2025); Kreutter and
Reymond (2023) or can be used with a user-defined reaction ontology for robotic or parallel chemistry (e.g., Dombrowski et al. (2022)).
More broadly, our framework demonstrates that LLMs can generate realistic synthetic datasets by mapping high-level chemical concepts directly to molecular structures, which in turn can guide applications such as the design of novel, synthetically feasible molecules in de novo drug design.
Ultimately, our methodology provides a general blueprint for applying LLM reasoning to various challenges in the molecular sciences where substructure identification is key, establishing atom-anchored LLMs as a powerful and data-efficient addition to the modern drug discovery toolbox.

#### Acknowledgements

This study was partially funded by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Innovative Training Network European Industrial Doctorate grant agreement No. 956832 “Advanced machine learning for Innovative Drug Discovery”.

#### Large Language Models

Large Language Models (LLMs) were used throughout the creation of this manuscript to improve spelling mistakes, grammar, and the overall reading flow. All LLM suggestions were profusely checked for correctness and refined by the authors of this work. The LLM was not used for any research-related tasks.

## References

- Achiam et al. [2023]
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya,
Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman,
Shyamal Anadkat, et al.
Gpt-4 technical report.
*arXiv preprint arXiv:2303.08774*, 2023.
- Boiko et al. [2023]
Daniil A. Boiko, Robert MacKnight, Ben Kline, and Gabe Gomes.
Autonomous chemical research with large language models.
*Nature*, 624(7992):570–578, December 2023.
ISSN 0028-0836, 1476-4687.
doi:[10.1038/s41586-023-06792-0](https://doi.org/10.1038/s41586-023-06792-0).
- M. Bran et al. [2024]
Andres M. Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D. White,
and Philippe Schwaller.
Augmenting large language models with chemistry tools.
*Nature Machine Intelligence*, 6(5):525–535, May 2024.
ISSN 2522-5839.
doi:[10.1038/s42256-024-00832-8](https://doi.org/10.1038/s42256-024-00832-8).
- Weininger [1988]
David Weininger.
SMILES, a chemical language and information system. 1.
Introduction to methodology and encoding rules.
*Journal of Chemical Information and Computer Sciences*,
28(1):31–36, February 1988.
ISSN 0095-2338, 1520-5142.
doi:[10.1021/ci00057a005](https://doi.org/10.1021/ci00057a005).
- Weininger et al. [1989]
David Weininger, Arthur Weininger, and Joseph L. Weininger.
SMILES. 2. Algorithm for generation of unique SMILES
notation.
*Journal of Chemical Information and Computer Sciences*,
29(2):97–101, May 1989.
ISSN 0095-2338, 1520-5142.
doi:[10.1021/ci00062a008](https://doi.org/10.1021/ci00062a008).
- Ross et al. [2022]
Jerret Ross, Brian Belgodere, Vijil Chenthamarakshan, Inkit Padhi, Youssef
Mroueh, and Payel Das.
Large-Scale Chemical Language Representations Capture Molecular
Structure and Properties, December 2022.
- Irwin et al. [2022]
Ross Irwin, Spyridon Dimitriadis, Jiazhen He, and Esben Jannik Bjerrum.
Chemformer: A pre-trained transformer for computational chemistry.
*Machine Learning: Science and Technology*, 3(1):015022, March 2022.
ISSN 2632-2153.
doi:[10.1088/2632-2153/ac3ffb](https://doi.org/10.1088/2632-2153/ac3ffb).
- Sadeghi et al. [2024]
Shaghayegh Sadeghi, Alan Bui, Ali Forooghi, Jianguo Lu, and Alioune Ngom.
Can large language models understand molecules?
*BMC Bioinformatics*, 25(1):225, June 2024.
ISSN 1471-2105.
doi:[10.1186/s12859-024-05847-x](https://doi.org/10.1186/s12859-024-05847-x).
- Masood et al. [2025]
Muhammad Arslan Masood, Samuel Kaski, and Tianyu Cui.
Molecular property prediction using pretrained-BERT and
Bayesian active learning: A data-efficient approach to drug design.
*Journal of Cheminformatics*, 17(1):58,
April 2025.
ISSN 1758-2946.
doi:[10.1186/s13321-025-00986-6](https://doi.org/10.1186/s13321-025-00986-6).
- Kim et al. [2024]
Seongmin Kim, Yousung Jung, and Joshua Schrier.
Large Language Models for Inorganic Synthesis Predictions,
June 2024.
- Cavanagh et al. [2024]
Joseph M. Cavanagh, Kunyang Sun, Andrew Gritsevskiy, Dorian Bagni, Thomas D.
Bannister, and Teresa Head-Gordon.
SmileyLlama: Modifying Large Language Models for Directed
Chemical Space Exploration, September 2024.
- Wei et al. [2023]
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia,
Ed Chi, Quoc Le, and Denny Zhou.
Chain-of-Thought Prompting Elicits Reasoning in Large Language
Models, January 2023.
- Guo et al. [2025]
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu,
Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al.
Deepseek-r1: Incentivizing reasoning capability in llms via
reinforcement learning.
*arXiv preprint arXiv:2501.12948*, 2025.
- Narayanan et al. [2025]
Siddharth M. Narayanan, James D. Braza, Ryan-Rhys Griffiths, Albert Bou, Geemi
Wellawatte, Mayk Caldas Ramos, Ludovico Mitchener, Samuel G. Rodriques, and
Andrew D. White.
Training a Scientific Reasoning Model for Chemistry, June
2025.
- [15]
Mistralai/Mistral-Small-24B-Instruct-2501 $\cdot$ Hugging Face.
https://huggingface.co/mistralai/Mistral-Small-24B-Instruct-2501.
- Qian et al. [2023]
Chen Qian, Huayi Tang, Zhirui Yang, Hong Liang, and Yong Liu.
Can Large Language Models Empower Molecular Property Prediction?,
July 2023.
- Guo et al. [2023]
Taicheng Guo, Kehan Guo, Bozhao Nan, Zhenwen Liang, Zhichun Guo, Nitesh V
Chawla, Olaf Wiest, and Xiangliang Zhang.
What can Large Language Models do in chemistry? A
comprehensive benchmark on eight tasks.
In *Proceedings of the 37th International Conference on
Neural Information Processing Systems*, New Orleans, LA, USA, 2023.
Curran Associates Inc.
- Ouyang et al. [2024]
Siru Ouyang, Zhuosheng Zhang, Bing Yan, Xuan Liu, Yejin Choi, Jiawei Han, and
Lianhui Qin.
Structured Chemistry Reasoning with Large Language Models,
February 2024.
- Segler et al. [2018]
Marwin H.S. Segler, Mike Preuss, and Mark P. Waller.
Planning chemical syntheses with deep neural networks and symbolic
AI.
*Nature*, 555(7698):604–610, 2018.
ISSN 1476-4687.
doi:[10.1038/nature25978](https://doi.org/10.1038/nature25978).
- Corey and Cheng [1989]
E. J. Corey and Xue-Min Cheng.
*The Logic of Chemical Synthesis*.
John Wiley & Sons, Ltd, New York, 1989.
ISBN 978-0-471-50979-0.
- Bran et al. [2025]
Andres M. Bran, Theo A. Neukomm, Daniel P. Armstrong, Zlatko Jončev, and
Philippe Schwaller.
Chemical reasoning in LLMs unlocks strategy-aware synthesis
planning and reaction mechanism elucidation, July 2025.
- Wang et al. [2025]
Haorui Wang, Jeff Guo, Lingkai Kong, Rampi Ramprasad, Philippe Schwaller,
Yuanqi Du, and Chao Zhang.
LLM-Augmented Chemical Synthesis and Design Decision
Programs, May 2025.
- Torren-Peraire et al. [2024]
Paula Torren-Peraire, Alan Kai Hassen, Samuel Genheden, Jonas Verhoeven,
Djork-Arné Clevert, Mike Preuss, and Igor V. Tetko.
Models Matter: The impact of single-step retrosynthesis on
synthesis planning.
*Digital Discovery*, 3(3):558–572, 2024.
ISSN 2635-098X.
doi:[10.1039/D3DD00252G](https://doi.org/10.1039/D3DD00252G).
- Li et al. [2025]
Hao Li, He Cao, Bin Feng, Yanjun Shao, Xiangru Tang, Zhiyuan Yan, Li Yuan,
Yonghong Tian, and Yu Li.
Beyond Chemical QA: Evaluating LLM’s Chemical Reasoning
with Modular Chemical Operations, June 2025.
- Tetko et al. [2020]
Igor V Tetko, Pavel Karpov, Ruud Van Deursen, and Guillaume Godin.
State-of-the-art augmented NLP transformer models for direct and
single-step retrosynthesis.
*Nature Communications*, 11(1):5575, 2020.
doi:[10.1038/s41467-020-19266-y](https://doi.org/10.1038/s41467-020-19266-y).
- Chen and Jung [2021]
Shuan Chen and Yousung Jung.
Deep Retrosynthetic Reaction Prediction using Local
Reactivity and Global Attention.
*JACS Au*, 1(10):1612–1620, 2021.
doi:[10.1021/jacsau.1c00246](https://doi.org/10.1021/jacsau.1c00246).
- Zhong et al. [2023]
Weihe Zhong, Ziduo Yang, and Calvin Yu-Chian Chen.
Retrosynthesis prediction using an end-to-end graph generative
architecture for molecular graph editing.
*Nature Communications*, 14(1):3009, May
2023.
ISSN 2041-1723.
doi:[10.1038/s41467-023-38851-5](https://doi.org/10.1038/s41467-023-38851-5).
- Igashov et al. [2024]
Ilia Igashov, Arne Schneuing, Marwin Segler, Michael Bronstein, and Bruno
Correia.
Retrobridge: Modeling retrosynthesis with markov bridges.
2024.
- Yang et al. [2024]
Yifei Yang, Runhan Shi, Zuchao Li, Shu Jiang, Bao-Liang Lu, Yang Yang, and Hai
Zhao.
BatGPT-Chem: A Foundation Large Model For Retrosynthesis
Prediction, August 2024.
- Nguyen-Van et al. [2024]
Phuong Nguyen-Van, Long Nguyen Thanh, Ha Hoang Manh, Ha Anh Pham Thi, Thanh
Le Nguyen, and Viet Anh Nguyen.
Adapting Language Models for Retrosynthesis Prediction, July
2024.
- Thakkar et al. [2023]
Amol Thakkar, Alain C. Vaucher, Andrea Byekwaso, Philippe Schwaller, Alessandra
Toniato, and Teodoro Laino.
Unbiasing Retrosynthesis Language Models with Disconnection
Prompts.
*ACS Central Science*, 9(7):1488–1498, July
2023.
ISSN 2374-7943, 2374-7951.
doi:[10.1021/acscentsci.3c00372](https://doi.org/10.1021/acscentsci.3c00372).
- Kreutter and
Reymond [2023]
David Kreutter and Jean-Louis Reymond.
Multistep retrosynthesis combining a disconnection aware triple
transformer loop with a route penalty score guided tree search.
*Chemical Science*, 14(36):9959–9969, 2023.
ISSN 2041-6520, 2041-6539.
doi:[10.1039/D3SC01604H](https://doi.org/10.1039/D3SC01604H).
- Westerlund et al. [2025]
Annie M. Westerlund, Lakshidaa Saigiridharan, and Samuel Genheden.
Human-guided synthesis planning *via* prompting.
*Chemical Science*, 16(32):14655–14667,
2025.
ISSN 2041-6520, 2041-6539.
doi:[10.1039/D5SC00927H](https://doi.org/10.1039/D5SC00927H).
- Yang et al. [2025]
An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen
Yu, Chang Gao, Chengen Huang, Chenxu Lv, Chujie Zheng, Dayiheng Liu, Fan
Zhou, Fei Huang, Feng Hu, Hao Ge, Haoran Wei, Huan Lin, Jialong Tang, Jian
Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jing Zhou,
Jingren Zhou, Junyang Lin, Kai Dang, Keqin Bao, Kexin Yang, Le Yu, Lianghao
Deng, Mei Li, Mingfeng Xue, Mingze Li, Pei Zhang, Peng Wang, Qin Zhu, Rui
Men, Ruize Gao, Shixuan Liu, Shuang Luo, Tianhao Li, Tianyi Tang, Wenbiao
Yin, Xingzhang Ren, Xinyu Wang, Xinyu Zhang, Xuancheng Ren, Yang Fan, Yang
Su, Yichang Zhang, Yinger Zhang, Yu Wan, Yuqiong Liu, Zekun Wang, Zeyu Cui,
Zhenru Zhang, Zhipeng Zhou, and Zihan Qiu.
Qwen3 Technical Report, May 2025.
- Comanici et al. [2025]
Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen
Sachdeva, Inderjit Dhillon, Marcel Blistein, Ori Ram, Dan Zhang, Evan Rosen,
et al.
Gemini 2.5: Pushing the frontier with advanced reasoning,
multimodality, long context, and next generation agentic capabilities.
*arXiv preprint arXiv:2507.06261*, 2025.
- Anthropic [2025]
Anthropic.
Claude 4 System Card.
https://www-cdn.anthropic.com/6d8a8055020700718b0c49369f60816ba2a7c285.pdf,
2025.
- OpenAI [2025]
OpenAI.
GPT-5 System Card.
https://cdn.openai.com/gpt-5-system-card.pdf, 2025.
- Lowe [2012]
Daniel Mark Lowe.
*Extraction of Chemical Structures and Reactions from the
Literature*.
Thesis, University of Cambridge, 2012.
- Schneider et al. [2016]
Nadine Schneider, Nikolaus Stiefl, and Gregory A. Landrum.
What’s What: The (Nearly) Definitive Guide to
Reaction Role Assignment.
*Journal of Chemical Information and Modeling*, 56(12):2336–2346, 2016.
doi:[10.1021/acs.jcim.6b00564](https://doi.org/10.1021/acs.jcim.6b00564).
- Genheden and Bjerrum [2022]
Samuel Genheden and Esben Bjerrum.
PaRoutes: Towards a framework for benchmarking retrosynthesis
route predictions.
*Digital Discovery*, 1(4):527–539, 2022.
doi:[10.1039/D2DD00015F](https://doi.org/10.1039/D2DD00015F).
- Somnath et al. [2021]
Vignesh Ram Somnath, Charlotte Bunne, Connor Coley, Andreas Krause, and Regina
Barzilay.
Learning Graph Models for Retrosynthesis Prediction.
In *Advances in Neural Information Processing Systems*,
volume 34, pages 9405–9415. Curran Associates, Inc., 2021.
- Dobbelaere et al. [2024]
Maarten R. Dobbelaere, István Lengyel, Christian V. Stevens, and Kevin M.
Van Geem.
Rxn-INSIGHT: Fast chemical reaction analysis using bond-electron
matrices.
*Journal of Cheminformatics*, 16(1):37,
March 2024.
ISSN 1758-2946.
doi:[10.1186/s13321-024-00834-z](https://doi.org/10.1186/s13321-024-00834-z).
- Jiang et al. [2023]
Ming Jiang, Mirjam C. W. Huizenga, Jonah L. Wirt, Janos Paloczi, Avand Amedi,
Richard J. B. H. N. Van Den Berg, Joerg Benz, Ludovic Collin, Hui Deng, Xinyu
Di, Wouter F. Driever, Bogdan I. Florea, Uwe Grether, Antonius P. A. Janssen,
Thomas Hankemeier, Laura H. Heitman, Tsang-Wai Lam, Florian Mohr, Anto
Pavlovic, Iris Ruf, Helma Van Den Hurk, Anna F. Stevens, Daan Van Der Vliet,
Tom Van Der Wel, Matthias B. Wittwer, Constant A. A. Van Boeckel, Pal Pacher,
Andrea G. Hohmann, and Mario Van Der Stelt.
A monoacylglycerol lipase inhibitor showing therapeutic efficacy in
mice without central side effects or dependence.
*Nature Communications*, 14(1):8039,
December 2023.
ISSN 2041-1723.
doi:[10.1038/s41467-023-43606-3](https://doi.org/10.1038/s41467-023-43606-3).
- Hassen et al. [2025]
Alan Kai Hassen, Helen Lai, Samuel Genheden, Mike Preuss, and Djork-Arné
Clevert.
Synthesis Planning in Reaction Space: A Study on
Success, Robustness and Diversity, June 2025.
- Dombrowski et al. [2022]
Amanda W. Dombrowski, Ana L. Aguirre, Anurupa Shrestha, Kathy A. Sarris, and
Ying Wang.
The Chosen Few: Parallel Library Reaction Methodologies for
Drug Discovery.
*The Journal of Organic Chemistry*, 87(4):1880–1897, February 2022.
ISSN 0022-3263, 1520-6904.
doi:[10.1021/acs.joc.1c01427](https://doi.org/10.1021/acs.joc.1c01427).
- Deng et al. [2017]
Hui Deng, Sander Kooijman, Adrianus M. C. H. Van Den Nieuwendijk, Daisuke
Ogasawara, Tom Van Der Wel, Floris Van Dalen, Marc P. Baggelaar, Freek J.
Janssen, Richard J. B. H. N. Van Den Berg, Hans Den Dulk, Benjamin F.
Cravatt, Herman S. Overkleeft, Patrick C. N. Rensen, and Mario Van Der Stelt.
Triazole Ureas Act as Diacylglycerol Lipase Inhibitors and
Prevent Fasting-Induced Refeeding.
*Journal of Medicinal Chemistry*, 60(1):428–440, January 2017.
ISSN 0022-2623, 1520-4804.
doi:[10.1021/acs.jmedchem.6b01482](https://doi.org/10.1021/acs.jmedchem.6b01482).
- Li et al. [2023]
Xiaoting Li, Hao Chang, Jara Bouma, Laura V. De Paus, Partha Mukhopadhyay,
Janos Paloczi, Mohammed Mustafa, Cas Van Der Horst, Sanjay Sunil Kumar, Lijie
Wu, Yanan Yu, Richard J. B. H. N. Van Den Berg, Antonius P. A. Janssen, Aron
Lichtman, Zhi-Jie Liu, Pal Pacher, Mario Van Der Stelt, Laura H. Heitman, and
Tian Hua.
Structural basis of selective cannabinoid CB2 receptor
activation.
*Nature Communications*, 14(1):1447, March
2023.
ISSN 2041-1723.
doi:[10.1038/s41467-023-37112-9](https://doi.org/10.1038/s41467-023-37112-9).
- Baggelaar et al. [2015]
Marc P. Baggelaar, Pascal J. P. Chameau, Vasudev Kantae, Jessica Hummel,
Ku-Lung Hsu, Freek Janssen, Tom Van Der Wel, Marjolein Soethoudt, Hui Deng,
Hans Den Dulk, Marco Allarà, Bogdan I. Florea, Vincenzo Di Marzo,
Wytse J. Wadman, Chris G. Kruse, Herman S. Overkleeft, Thomas Hankemeier,
Taco R. Werkman, Benjamin F. Cravatt, and Mario Van Der Stelt.
Highly Selective, Reversible Inhibitor Identified by
Comparative Chemoproteomics Modulates Diacylglycerol Lipase Activity in
Neurons.
*Journal of the American Chemical Society*, 137(27):8851–8857, July 2015.
ISSN 0002-7863, 1520-5126.
doi:[10.1021/jacs.5b04883](https://doi.org/10.1021/jacs.5b04883).
- Mock et al. [2020]
Elliot D. Mock, Mohammed Mustafa, Ozge Gunduz-Cinar, Resat Cinar, Gavin N.
Petrie, Vasudev Kantae, Xinyu Di, Daisuke Ogasawara, Zoltan V. Varga, Janos
Paloczi, Cristina Miliano, Giulia Donvito, Annelot C. M. Van Esbroeck, Anouk
M. F. Van Der Gracht, Ioli Kotsogianni, Joshua K. Park, Andrea Martella, Tom
Van Der Wel, Marjolein Soethoudt, Ming Jiang, Tiemen J. Wendel, Antonius
P. A. Janssen, Alexander T. Bakker, Colleen M. Donovan, Laura I. Castillo,
Bogdan I. Florea, Jesse Wat, Helma Van Den Hurk, Matthias Wittwer, Uwe
Grether, Andrew Holmes, Constant A. A. Van Boeckel, Thomas Hankemeier,
Benjamin F. Cravatt, Matthew W. Buczynski, Matthew N. Hill, Pal Pacher,
Aron H. Lichtman, and Mario Van Der Stelt.
Discovery of a NAPE-PLD inhibitor that modulates emotional
behavior in mice.
*Nature Chemical Biology*, 16(6):667–675,
June 2020.
ISSN 1552-4450, 1552-4469.
doi:[10.1038/s41589-020-0528-7](https://doi.org/10.1038/s41589-020-0528-7).

## 6 Appendix

### 6.1 Experimental Setup

**Table 2: A summary of the Large Language Models (LLMs) evaluated in this work. The table specifies whether the model is open-source, its status as a reasoning-optimized ("Thinking") variant, and its thinking budget allocation (in number of tokens) for closed-source models along with other parameters.**
| Source | Model Name | Thinking<br>model | Open-<br>Source | Model<br>quantization | Max output<br>length | Thinking<br>budget |
| --- | --- | --- | --- | --- | --- | --- |
| Yang et al. [2025] | Qwen3-4B-Thinking-2507 | yes | yes |  | 32768 | - |
| Narayanan et al. [2025] | ether0 (24B) | yes | yes |  | 32768 | - |
| Yang et al. [2025] | Qwen-3-30B-A3B-Thinking-2507 | yes | yes | 8bit | 32768 | - |
| Yang et al. [2025] | Qwen3-235B-A22B-Instruct-2507-FP8 | no | yes | 8bit | 32768 | - |
| Yang et al. [2025] | Qwen3-235B-A22B-Thinking-2507-FP8 | yes | yes | 8bit | 32768 | - |
| Guo et al. [2025] | RedHat-DeepSeek-R1-0528-w4a16 (670B) | yes | yes | 4bit | 32768 | - |
| Comanici et al. [2025] | Gemini 2.5 Flash | yes | no | API | 65536 | 30000 |
| Comanici et al. [2025] | Gemini 2.5 Pro | yes | no | API | 65536 | 30000 |
| Anthropic [2025] | Claude Sonnet 4 | yes | no | API | 64000 | 30000 |
| OpenAI [2025] | GPT5 | yes | no | API | 128000 | ’High’ |

Figure: Figure 5: Distribution of reaction names in the USPTO-50k test set. From this dataset, we created a balanced subsample (USPTO-LLM) for evaluation by selecting up to five examples per named reaction class, while maintaining the original proportion of the ’otherReaction’ class.
Refer to caption: x5.png

### 6.2 Position Model

**Table 3: A comprehensive comparison of various models based on several key performance metrics. The table highlights the average number of predictions, partial and exact match percentages, reaction accuracy, and the total number of successes and failures for each model. The best performance in each column is highlighted in bold.**
| Model | Avg. number<br>of predictions | Partial<br>match (%) | Exact<br>match (%) | Reaction<br>acc. (%) | Total<br>predictions | Failed<br>predictions |
| --- | --- | --- | --- | --- | --- | --- |
| Ether0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 541 |
| Qwen3-4B | 4.0 | 73.01 | 31.61 | 3.51 | 541 | 0 |
| Qwen3-30B | 3.8 | 74.86 | 34.75 | 14.23 | 541 | 0 |
| Gemini 2.5 Flash | 15.3 | 90.54 | 56.59 | 37.11 | 539 | 2 |
| Gemini 2.5 Flash (thinking) | 16.3 | 91.84 | 61.6 | 35.81 | 539 | 2 |
| Qwen3-235B-thinking | 5.9 | 88.5 | 58.07 | 25.97 | 539 | 2 |
| Qwen3-235B-instruct | 9.6 | 86.67 | 49.44 | 13.33 | 540 | 1 |
| DeepSeek-R1-670B | 7.3 | 87.25 | 53.23 | 29.21 | 541 | 0 |
| Claude Sonnet 4 | 10.0 | 92.57 | 58.55 | 39.03 | 538 | 3 |
| GPT-5 | 15.1 | 91.08 | 54.28 | 47.03 | 538 | 3 |
| Gemini 2.5 Pro | 11.1 | 91.87 | 66.54 | 40.3 | 541 | 0 |

Figure: Figure 6: Confusion matrix of predicted versus ground-truth reaction names for the Gemini 2.5 Pro model. The analysis is conditional, including only predictions where the model successfully identified at least a partial positional match. For this visualization, reactions outside the defined reaction ontology were excluded. The matrix was generated using the original class-to-name mappings from the ground-truth data, with any unassigned reactions grouped into the ’Miscellaneous’ category.
Refer to caption: x6.png

### 6.3 Transition Model

This section covers additional results on the transition model (reactant prediction).

**Table 4: A comparison of model performance on the transition task (reactant prediction). This table presents the total successful predictions, along with accuracy scores for reactants, templates, and combined category. The best performance in each column is highlighted in bold.**
| Model | Avg. number<br>of predictions | Reactants<br>accuracy | Template<br>accuracy | Combined<br>accuracy | Total<br>predictions | Failed<br>predictions |
| --- | --- | --- | --- | --- | --- | --- |
| Ether0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 541 |
| Qwen3-4B | 3.0 | 0.11 | 0.05 | 0.13 | 529 | 12 |
| Qwen3-30B | 3.6 | 0.22 | 0.12 | 0.27 | 535 | 6 |
| Gemini 2.5 Flash | 4.4 | 0.44 | 0.31 | 0.51 | 513 | 28 |
| Qwen3-235B-thinking | 4.4 | 0.56 | 0.29 | 0.61 | 522 | 19 |
| Qwen3-235B-instruct | 6.6 | 0.40 | 0.39 | 0.48 | 537 | 4 |
| DeepSeek-R1-670B | 4.4 | 0.64 | 0.28 | 0.70 | 528 | 13 |
| Claude Sonnet 4 | 5.0 | 0.65 | 0.39 | 0.71 | 515 | 26 |
| GPT-5 | 10.4 | 0.68 | 0.44 | 0.73 | 510 | 31 |
| Gemini 2.5 Pro | 5.7 | 0.75 | 0.44 | 0.81 | 512 | 29 |

Figure: Figure 7: Performance difference between known and unknown reaction names. For unknown reactions, no equivalent name reaction examples within the USPTO50k training dataset are provided, illustrating the importance of the reaction name as a chemical anchor for retrieving reaction examples and chemical reasoning.
Refer to caption: x7.png

Figure: Figure 8: An ablation study on the impact of prompt instruction detail and the inclusion of in-context examples on the performance of the Gemini 2.5 Pro transition model. We evaluate four settings: 1) a simple prompt without examples (see Appendix [3](https://arxiv.org/html/2510.16590v1#LST3)); 2) a detailed prompt without examples (see Appendix [2](https://arxiv.org/html/2510.16590v1#LST2)); 3) a simple prompt with examples; and 4) a detailed prompt with examples.
Refer to caption: x8.png

Figure: Figure 9: Confusion Matrix Highlighting the performance of different Transition Models on respective reaction name classes using either template or reactant accuracy. The reactions are sorted by the number of reaction examples available in the set (high-to-low).
Refer to caption: x9.png

### 6.4 Application Examples

Figure: Figure 10: Five real-world drug disovery molecules used in our case study: DH376 Deng et al. [2017], LEI-102 Li et al. [2023], LEI-105 Baggelaar et al. [2015], LEI-401 Mock et al. [2020], LEI-515 Jiang et al. [2023]
Refer to caption: x10.png

**Table 5: Predicted Disconnection Sites for LEI_515 Jiang et al. [2023]. Header descriptions are as follows: Prio.: Priority Ranking of the Disconnections; Position: The position where the disconnection is; Reaction: The forward reaction; Ontology: If the reaction is in the reaction ontology for which examples are available; Imp.: Retrosynthesis Importance, alignment with retrosynthesis goals; Rationale: The chemical rationale.**
| Prio. | Position | Reaction | Ontology | Imp. | Rationale |
| --- | --- | --- | --- | --- | --- |
| 1 | C:12 N:14 | Carboxylic acid to amide conversion | Yes | 4 | Identified via Inter-Fragment Analysis, this is a very high-impact, convergent disconnection linking the chiral piperazine amine and the aryl carboxylic acid core, drastically simplifying the molecule (Goals a, c). The forward reaction (amide coupling) is robust and high-yielding (Goal b). No major chemoselectivity issues are expected with modern coupling reagents. |
| 2 | C:12 N:14 | Acylation of Nitrogen Nucleophiles by Acyl/Thioacyl/Carbamoyl Halides and Analogs_N | Yes | 4 | Identified via Inter-Fragment Analysis, this is a very high-impact, convergent disconnection that simplifies the molecule into two key fragments (Goals a, c). Activation of the carboxylic acid as an acyl chloride is a classic, robust method for amide formation (Goal b), though it may require protection of other nucleophilic sites. |
| 3 | N:17 c:18 | Buchwald-Hartwig/Ullmann-Goldberg/N-arylation secondary amine | Yes | 4 | Identified via Inter-Fragment Analysis, this key convergent C-N bond formation builds the N-arylpiperazine motif (Goal c). The forward reaction, a Buchwald-Hartwig amination, is a powerful and reliable method for this transformation, simplifying the chiral amine precursor (Goals a, b). Potential for competitive reaction at N:14 necessitates a protecting group strategy. |
| 4 | N:17 c:18 | N-arylation (Buchwald-Hartwig/Ullmann-Goldberg) | Yes | 4 | Identified via Inter-Fragment Analysis, this strategic disconnection simplifies the chiral piperazine component by removing the aryl group (Goal a). The forward N-arylation is a powerful and reliable reaction for constructing this key bond (Goals b, c). Regioselectivity between the two piperazine nitrogens is a key issue that must be controlled, likely with a protecting group on N:14. |
| 5 | C:5 S:6 | S-alkylation of thiols | Yes | 3 | Identified via Strategic Bond Analysis. This disconnects the beta-keto sulfoxide side chain from the aryl core (Goal c). The forward synthesis involves S-alkylation of the corresponding thiophenol with an alpha-halo ketone precursor, followed by oxidation. This is a robust way to build this key C-S bond, but the electrophile itself requires separate synthesis (Goals a, b). |
| 6 | c:11 C:12 | Grignard with CO2 to carboxylic acid | Yes | 3 | Identified via FGI Analysis. Strategic C-C bond formation to install the carboxyl group on the central aromatic ring (Goal c). The forward synthesis via carboxylation of an organometallic (e.g., Grignard) from an aryl halide is classic and reliable (Goals a, b). Chemoselectivity is a concern, as formation of the organometallic must be compatible with the sulfide/sulfoxide group. |
| 7 | S:6 O:7 | Sulfanyl to sulfinyl_H2O2 | Yes | 2 | Identified via FGI Analysis. Standard FGI to install the sulfoxide from a more stable and easier to handle sulfide precursor. The oxidation can be performed late-stage, but requires careful control of conditions to prevent over-oxidation to the sulfone, which presents a chemoselectivity challenge (Goal b). |
| 8 | C:2 F:32 F:33 | Fluorination | Yes | 2 | Identified via FGI Analysis. This disconnection corresponds to a forward reaction installing the difluoro moiety. Electrophilic fluorination of the beta-keto sulfoxide enolate is a viable route (Goal c). Chemoselectivity could be an issue due to multiple acidic protons (at C:5) and potential for mono- vs di-fluorination, requiring kinetic control. |
| 9 | c:30 Cl:31 | Aromatic chlorination | Yes | 2 | Identified via FGI Analysis. This FGI installs the chloro substituent via electrophilic aromatic substitution (Goal c). The regioselectivity of the chlorination would be directed by the existing sulfoxide/sulfide and carboxylate/amide groups. Predicting and controlling the outcome relative to other open positions on the ring requires careful consideration of the combined directing effects. |
| 10 | c:20 Cl:21 | Aromatic chlorination | Yes | 2 | Identified via FGI Analysis. This FGI installs the chloro substituent on the N-aryl ring via electrophilic aromatic substitution (Goal c). The reaction would be strongly directed by the activating amine substituent, likely leading to the observed para-chlorination, making this a reliable and predictable transformation (Goal b). |
| 11 | C:3 O:4 | Oxidation or Dehydrogenation of Alcohols to Aldehydes and Ketones | Yes | 2 | Identified via FGI analysis. Standard FGI to form the ketone from a secondary alcohol precursor. While many mild oxidation reagents are available, the presence of the easily oxidizable sulfoxide (or its sulfide precursor) on the same molecule presents a major chemoselectivity challenge that must be carefully managed (Goal b). |
| 12 | C:12 O:13 N:14 | Nitrile to amide | Yes | 2 | Identified via FGI analysis. This transforms the amide into a nitrile precursor, offering an alternative synthetic route to the central aromatic core (Goal a). A nitrile can be introduced via methods like the Sandmeyer reaction. The forward reaction, partial hydrolysis of the nitrile to the amide, can be challenging to stop without proceeding to the carboxylic acid. |
| 13 | N:14 | Boc amine deprotection | Yes | 1 | Identified via Protecting Group Analysis. This is a tactical deprotection step. A protecting group like Boc on N:14 would be crucial in a forward synthesis to ensure regioselective N-arylation at N:17. This step reveals the nucleophilic amine for the final amide coupling and is a common, practical consideration (Goal d). |
| 14 | C:2 C:3 | Enolate Acylation | No | 3 | Identified via Strategic Bond Analysis. This strategic C-C bond disconnection breaks down the beta-keto side chain (Goal a). The forward reaction, likely an enolate acylation, is a powerful method for ketone synthesis (Goal c). However, generating and controlling the reactivity and stability of the required difluoroenolate precursor could be challenging. |

Figure: Figure 11: Correct reactant prediction for LEI-515 Jiang et al. [2023] by the Transition model (position priority 3, transition prediction 3).
Refer to caption: x11.png

Figure: Figure 12: Correct reactant template prediction for LEI-515 Jiang et al. [2023] by the Transition model (position priority 3, transition prediction 1).
Refer to caption: x12.png

Figure: Figure 13: Correctly flagged chemically invalid prediction for LEI-515 Jiang et al. [2023] by the Transition Model (position priority 13, transition prediction 1).
Refer to caption: x13.png

Figure: Figure 14: Syntactically invalid SMILES prediction for LEI-515 Jiang et al. [2023] by the Transition model (position priority 1, transition prediction 4).
Refer to caption: x14.png

### 6.5 Application Questionaire

**Table 6: Full list of questions for the expert validation study. These are the complete, verbatim questions presented to chemists to benchmark the performance of our framework. The evaluation was split into two parts: assessing the disconnection sites proposed by the Position (P) model and the final reactant structures generated by the Transition (T) model.**
| Q. | Description |
| --- | --- |
| P1 | Is the suggested disconnection position chemically plausible (i.e., not violating fundamental principles)? |
| P2 | Is the suggested reaction name a correct label for the proposed disconnection position? |
| P3 | Is the provided chemical reasoning for the suggested disconnection (position and reaction name) scientifically sound? |
| P4 | Considering all the provided information, could this suggested step realistically work in a laboratory setting? |
| P5 | Has this specific transformation actually been performed successfully in practice for the molecule? |
| P6 | Are there any strategically important disconnections that are obviously missing from this prediction? |
| T1 | Given the transition prediction includes a reaction template, does the reaction template capture the overall chemical transformation of the reaction? |
| T2 | Given the transition prediction includes a reaction template, does the chemical reasoning for the reaction template align with the underlying reaction? |
| T3 | Among the reactant predictions, is there at least one that provides a chemically correct set of reactants to form the target product? |
| T4 | If the model predicts a chemically correct set of reactants, is the model’s chemical reasoning for that specific set of reactants correct? |
| T5 | If the reaction was conducted in the lab, does the model correctly predict the set of reactants that were used in the lab? |
| T6 | If the model flags one of its own predictions as ’chemically invalid’, is its reasoning for that assessment correct? |
| T7 | How many reactants are predicted as chemically valid and are not reaction templates are correct? |

**Table 7: Detailed reponse data.**
| ID | P1 | P2 | P3 | P4 | P5 | P6 | T1 | T2 | T3 | T4 | T5 | T6 | T7 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| DH376 Deng et al. [2017] | 12/13 | 11/13 | 8/13 | 11/13 | 5/13 | 1 | 4/4 | 4/4 | 4/4 | 4/4 | 3/4 | 2/2 | 23/31 |
| LEI-102 Li et al. [2023] | 14/16 | 12/16 | 12/16 | 14/16 | 2/16 | 1 | 3/3 | 3/3 | 4/4 | 4/4 | 4/4 | 3/3 | 18/18 |
| LEI-105 Baggelaar et al. [2015] | 8/9 | 8/9 | 8/9 | 5/9 | 2/9 | 1 | 2/2 | 2/2 | 2/2 | 2/2 | 1/1 | - | 11/11 |
| LEI-401 Mock et al. [2020] | 11/11 | 11/11 | 7/11 | 11/11 | 2/11 | 0 | 2/3 | 3/3 | 3/3 | 3/3 | 2/2 | 1/1 | 10/15 |
| LEI-515 Jiang et al. [2023] | 12/14 | 12/14 | 11/14 | 8/14 | 5/14 | 1 | 2/4 | 2/4 | 4/6 | 4/6 | 1/4 | 1/1 | 11/23 |
| Acc. | 90.5 | 85.7 | 73.0 | 77.8 | 25.4 | 80.0 | 81.3 | 87.5 | 89.5 | 89.5 | 73.3 | 1 | 74.5 |

### 6.6 Prompts

#### 6.6.1 Position Model

Figure: Listing 1: Position Model Prompt.

#### 6.6.2 Transition Model

Note: This prompts is slightly altered for visualization purposes.

Figure: Listing 2: Transition Model Prompt.

Figure: Listing 3: Ablation Study Short Transition Model Prompt.

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: See weaknesses
soundness: 3
strengths: The authors provide a new method to improve the analytic capabilities of LLMs, and show that this improves dramatically their applicability on complex chemical analysis tasks.
the evaluation is thorough and uses public benchmarks and datasets, evaluates different LLMs both closed, open and across multiple scales. the prompts are released as well which contributes to reproducibility.
summary: The paper proposes a method for leveraging LLMs for tasks in molecular analysis and specifically single-step retrosynthesis. The method relies on atom-mapping syntax for SMILES strings, and uses LLMs as evaluators at the level of atoms and bonds. The framework then uses these fine-grained evaluations to judge the potential for disconnection, and then to predict the reaction that could be used to materialize the disconnection.
Interesting idea overall.
weaknesses: Looking at the sizes of the datasets used for evaluation, it would be important to do a discussion on costs and latencies of the proposed methodology. For instance, how many LLM calls are required per problem? how does it scale? e.g. it seems like the method scales with the number of atoms in the molecule. Please discuss this further.

The authors describe the advantages of the method more as a way of collecting synthetic data given how scarce real data is in specific fields. It would be a very interesting contribution, and would really make a better case for this claim, if some generated dataset was released for use of the community. Or if you trained another model, or analyzed the data, etc. Something related to creating a dataset and demonstrate its usefulness. I believe a lot of data was already created during the creation of the results presented here, so shouldn't be very problematic.

---

confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
questions: * Can the method generalize to multi-step synthesis and stereoselective reactions?

* Would fine-tuning on limited chemistry-specific reasoning traces further improve accuracy?
soundness: 3
strengths: * The framework requires no labeled data, making it highly adaptable to data-scarce chemical domains.

* The atom-level anchoring enhances interpretability and aligns model reasoning with real chemical logic.

* The approach generalizes across benchmarks and real drug molecules, showing practical applicability.
summary: This paper proposes an atom-anchored reasoning framework that enables general large language models to perform chemically interpretable retrosynthesis without any task-specific training data. By introducing atom-level mappings and a two-stage chain-of-thought process, the model achieves strong performance in identifying reaction centers, classifying reaction types, and predicting reactants, demonstrating its capability to reason over molecular structures in a transparent and generalizable way.
weaknesses: * The model's performance relies heavily on well-crafted prompts and example selection.

* It struggles with edge cases involving unusual reaction mechanisms or rare functional groups.

---

confidence: 3
contribution: 3
initial_rating: 4
presentation: 4
questions: Can this idea be generalized to more complicated tasks, e.g., molecules?

My understanding is that the LLM is not trained on chemical data but only on language data. What is the intuition that LLM transfers its knowledge to such a different area, without finetuning?

What is the inference cost of this LLM approach compared to existing, traditional approaches?
soundness: 3
strengths: The paper is well written, and the figures are helpful.
summary: The paper presents a novel, label-free framework for molecular reasoning using general-purpose LLMs that operates without requiring labeled training data. LLM's chain-of-thought is applied to the molecular structure by using unique atomic identifiers, which enables the one-shot identification of relevant chemical fragments and transformation classes for the retrosynthesis task. This approach is highly significant as it bypasses the need for large, labeled datasets. The algorithm achieves high success rates test on both academic benchmarks and expert-validated drug discovery molecules.
weaknesses: See questions


Decision: Reject