RetroInText: A Multimodal Large Language Model Enhanced Framework for Retrosynthetic Planning via In-Context Representation Learning
Authors: Chenglong Kang
Xiaoyi Liu
Fei Guo
Venue: ICLR 2025 Poster (2025)
Keywords: Retrosynthetic Planning
Route Evaluation
In-Context Learning
Large Language Model
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: Multimodal Model Enhanced Framework for Retrosynthetic Planning via In-Context Representation Learning

Abstract: Development of robust and effective strategies for retrosynthetic planning requires a deep understanding of the synthesis process. A critical step in achieving this goal is accurately identifying synthetic intermediates. Current machine learning-based methods often overlook the valuable context from the overall route, focusing only on predicting reactants from the product, requiring cost annotations for every reaction step, and ignoring the multi-faced nature of molecular, resulting in inaccurate synthetic route predictions. Therefore, we introduce RetroInText, an advanced end-to-end framework based on a multimodal Large Language Model (LLM), featuring in-context learning with TEXT descriptions of synthetic routes. First, RetroInText including ChatGPT presents detailed descriptions of the reaction procedure. It learns the distinct compound representations in parallel with corresponding molecule encoders to extract multi-modal representations including 3D features. Subsequently, we propose an attention-based mechanism that offers a fusion module to complement these multi-modal representations with in-context learning and a fine-tuned language model for a single-step model. As a result, RetroInText accurately represents and effectively captures the complex relationship between molecules and the synthetic route. In experiments on the USPTO pathways dataset RetroBench, RetroInText outperforms state-of-the-art methods, achieving up to a 5% improvement in Top-1 test accuracy, particularly for long synthetic routes. These results demonstrate the superiority of RetroInText by integrating with context information over routes. They also demonstrate its potential for advancing pathway design and facilitating the development of organic chemistry. Code is available at https://github.com/guofei-tju/RetroInText.

Published as a conference paper at ICLR 2025
RETROINTEXT:
A MULTIMODAL LARGE LANGUAGE MODEL ENHANCED FRAMEWORK FOR RETROSYN- THETIC PLANNING VIA IN-CONTEXT REPRESENTA-
TION LEARNING
Chenglong Kang' Xiaoyi Liu Fei Guo
Schooo of Computer Science and Engineering, Central South University
Schooo of Chinese Materia Medica, Beijing University of Chinese Medicine xiaoyi liu@bucm. edu. on guofei Ccsuccusee..... edu.
ABSTRACT
Development of robust and effective strategies for retrosynthetic plamning re- quires deep understanding the synthesis process. A critical step in achiev- ing this goal is accurately identifying synthetic intermediates. Current machine learning-base methods often overlook the valuable context from the overall route, focusing only on predicting reactants from the product, requiring cost annota- tions for every reaction step, and ignoring the multi-faced nature molecu- lar, resulting in inaccurate synthetic route predictions. Therefore, introduce Retx an advanced end-to-end framework based on a multimodal Large Language Model (LLM), featuring in-context learning with TEXT descriptions of synthetic routes. First, RetrolnTText including ChatGPT presents detailed de- scriptions of the reaction procedure. It learns the distinet compound represen- tations in parallel with corresponding molecule encoders to extract multi-modal representations including 3D features. Subsequently, we propose an attention- based mechanism that offers a fusion module to complement these multi-modal representations with in-context learning and fine-tuned language model for a single-step model. As result, RetrolnTeext accurately represents and effec- tively captures the complex relationship between molecules and the synthetic route. In experiments on the USPTO pathways dataset RetroBench, RetroIn- Text outperforms statc-of-the-art methods, achieving up to a 5% improvement in Top-1 test accuracy, particularly for long synthetic routes. These results demonstrate the superiority of RetroInText by integrating with context informa- tion over routes. They also demonstrate its potential for advancing pathway de- sign and facilitating the development of organic chemistry. Code is available at httt::://gittmbb..om/mopreeeesep
INTRODUCTION
Retrosynthesis stands an essential strategy in organic chemistry, critically important for advance- ments in drug discovery and chemical biology (Corey) 1991 Zheng et al. (022). Single-step ret- rosynthesis refers to predicting a single reaction step that breaks down target molecule into simpler, more accessible precursors (Jiang et al. 2023). Recent advances in deep learning have facilitated the development of various approaches in single-step retrosynthesis, which can be categorized as and template-free approaches (Zhong et al. 2023 Obonyo ct al. 2023 Chen et al.) 2020) Coley et al.) (007).. Specifically, significant advancements have been achieved in single-step models through encompassing sequence-based, graph-based and text- based methods. Sequence-bass based strategies leverage Simplified Molecular Input Line Entry System (SMILES) notation to represent reactions sequential tokens, facilitating transformer-base chitectures to predict the precursors for a target molecule. Graph-based approaches, conversely,
template-based,
"Corresponding Author.

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prioritize explicit molecular graph representations to model atomic connectivity and bond dynam- ies, enabling precise identification of reaction centers and transformation patterns. Graph Neural Networks (GNNs) have emerged as particularly effective tools in this domain due to their capacity to encode topological and physicochemical features. From text-based perspective, inspired by Nat- ural Language Processing (NLP) paradigms, text information associated with reactions is utilized to augment the model's 's comprehension of reaction mechanisms or to facilitate the reordering of single- step reaction predictions based on the insights derived from the textual data. Nevertheless, existing methods primarily rely on graph or SMILES representations, and are often limited in capturing the intricate complexities of chemical structures, thereby constraining their scalability and effectiveness in addressing complex retrosynthetic challenges.
Multi-step retrosynthesis planning is a fundamental strategy in organic chemistry, crucial for drug discovery and chemical biology, as it systematically breaks down complex target molecules into simpler, easily accessible precursors (Zheng et al. [022] Zhong et al. (023). Most existing retrosyn- thetic planning strategies (Tripp et 2024| Liu et al. (224cc) conceptualize retrosynthetic planning as a search problem, where the synthetic route is represented as a tree or graph, with molecules as nodes. However, significant limitation of these approaches lies in their reliance on heuristic search algorithms to determine which nodes (molecules) should be expanded. This dependency often leads to several critical challenges, such as ensuring that the expanded nodes commercially available compounds, avoiding computational inefficiencies, and maintaining the overall feasibility of the synthetic routes (Liu et al. 2023a). Additionally, due the complex chemical space, each molecule can exhibit a large number of potential transformations-uup to 10K (Szymkuć et al. 2016). The depth of the search tree, which corresponds the route length, often varies between 10 to 20 steps, depending on the complexity of the target molecule (Obonyo et al. 2023). This vast combinato- rial space, combined with the scarcity of high-quality structure data for retrosynthetic tasks, limits current methods ability to effectively explore and prioritize routes, leading to inefficiencies and sub-optimal solutions.
Inspired by the success of Large Language Models (LLM), which trained on extensive text corpora, generating coherent text encompassing a wide range of topics and sentiments (Ying et al. 202I). Liu et al. (Liu et al., 2024d) introduce a text-assisted retrosynthesis planning method that utilizes pre-trained language models to aid reactant generation. In addition, Bran et al. (M. Bran et al, 2024) propose ChemCrow, by integrating 17 expert-designed tools, ChemCrow enhances the LLM performance in chemistry. However, the current LLM methods do not include a route length adjust- ment to guide future searches (Liu et al. 2023a Despite these advancements, current LLM-based methods exhibit notable limitations, particularly the lack of an effective adaptation mechanism for route length, which is critical for guiding retrosynthetic planning (Liu et al.. 2023a).
In particular, we first use ChatGPT to obtain a description of the entire pathway, starting with the target product based on its name for single-step retrosynthesis. This textual description, along with the molecular 3D geometry information is used as input information for training. For each selection step in multi-step retrosynthesis, we introduce multiple value functions, such as Synthetic Complex- ity Score (ScScore) (Coley et al. 2018)) and the text captioning score to rank candidate reactants. We employ an existing pre-trained MolT5 as our single-step approach to intermediate prediction. Therefore, Retroln is context-aware model that integrates molecular captioning and context embeddings. RetroIn Text utilizes contextual information from previous steps for the entire pathway, thereby enhancing retrosynthesis prediction accuracy.
We evaluated RetroIn on the RetroBench dataset constructed by Liu et al. (Liu et al. 2023a), de- termining all possible synthetic routes for each target, resulting in comprehensive set of routes for 128,469 469 molecules. RetroBench dataset is constructed based on the USPTO-full dataset (Chen et al. 2020). Extensive experimental results on retrosynthetic planning tasks demonstrate that RetroIn- Text outperforms template-fiee frree baselines, achieving up to 5% improvement in Top-1 test accuracy. Additionally, ablation experiments confirm the effectiveness of textual information and LLM. We highlight our main contributions as follows:
We propose the RetroInText framework as a template-free approach for retrosynthesis pre- diction. When predicting subsequent steps in retrosynthesis, this framework integrates in-context textual information from previous steps.
With RetroInText, we leverage the advantage of LLM and ChatGPT as our generative mod- els and evaluate the reactions based their molecular descriptions. A combination of
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textual information, molecular graphs, and 3D geometry information is used to select the optimal molecule in the selection phase.
Extensive experiments have demonstrated that RetroInTextt achieves a competitive level of performance. Furthermore, RetrolnText is tested in experiments to show its ability to predict complex reactions.
RELATED WORK
Single Step Retrosynthesis. Existing single-step retrosynthesis methods are categorized into template-basedd cumi-template-basee and template-free approaches. Template-based methods ex- tract reaction templates from chemical reaction databases and model retrosynthesis classification or template retrieval task, mapping the product to reactants using predicted templates (Gaiñski et al.)
2024 Chen & Jung, 202T Xie et al., 2023 Zhang et al. 2024a). Semi-template- based methods de- compose the retrosynthesis problem into two steps. Including identifying reaction centers to gener- synthons, and converting these synthons into reactants using generative models adding leaving groups (Zhong et al. 2023) Somnath et al. [021 Zhu et al. 2023: Lan et al. 2024). Template- free free
methods treat retrosynthesis either sequence sequencceto--sqquenee co-sequence task using SMILES or graph-editing
task to modify atoms and bonds (Igashov et al., 2024; Andronov et al. 2024 Laabid et al.) [2024]
Yao et al. 2024 Liu et al. 2024d ZZang et al. 2024b). With the development of multimodal I.I.Ms, reasoning capabilities are being extended to retrosynthesis (M. Bran et al.) 2024). Although textual
information from LLM such as ChatGPT has been employed in single-step retrosynthesis models
QQan et al. 2023 Liu et al. 2024d), its integration into multi-step retrosynthesis processes remains unexplored Christofidellis et al. 2023 Liu et al. 2023b).
Retrosynthesis Planning. Retrosynthesis planning employs search algorithms identify optimal candidates from single-step model predictions iteratively until all target compounds are sourced from existing commercial suppliers (Liu et al. 2023c Zhao al., 2023 Liu al.. 2024a Zhang al., 2024b Zeng et al. 2024). These search a algorithms can broadly be categorized into several types: Monte Carlo Tree Search (MCTS) employs policy network to enhance retrosynthetic planning efficiency by effectively exploring and navigating the solution space (Segler et al., 2018). Retro* (Chen al. 2020) proposes an AND-OR retrosynthesis planning model using an A Aiklike heuris- tic, where OR nodes (reactions) require any child, and AND nodes (products) require all children. Modeling retrosynthesis planning as an AND-OR tree has proven sound and effective. Recent works have focused on developing active frameworks (Torren- Peraire et al.) 2024 Yuan et al.) 2024) and new evaluation methods (Tripp et al., 2024 Tian et al. 2024| Maziarz et al. 2024). For example, (Schreck et al. 2019) and (Liu et al.) assign uniform cost of to each reaction, optimizing for the shortest route. However, shorter routes may result in lower yields compared to longer routes. Con- sequently, (Liu et al. 2023a) propose novel single-step approach based on conventional search algorithm, but it lacks an adaptation mechanism for route length and full-route information. The aforementioned methodologies require the annotation of costs for every reaction step, and incorpo- rating reliable reaction quality data from chemists or laboratory experimentation entails significant expenses. As a result, these approaches often become economically impractical.
PRELIMINARY
3.1 SINGLE-STEP RETROSYNTHESIS
Define the space of all molecules M. The single-step retrosynthesis aims to input target molecule T E M, resulting in a prediction of the potential reactions and their related reactants outcomes. We denote it an injection:
0(): T (R, c(R))
where O(-) represents the single-step model, which outputs at most l reactions R, with their fol- lowing reactant sets I₁ and costs oR,,). The costs can be the actual price of the reaction or just a negative likeliiokho of this reaction under the model.
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A.Muttiple Step Search Model Workflow
Product
Feature Embedding
Single Step Model
Evaluation
Reactants
Netrics
FFeature Embedding
Product OccIcccc(0C=C((C)()))e
Molecular Generation
Response:
nyz
dichloree eenoxyppennyee ring affached hydroxyme thyt group and 2,2- bichoroothhnaww group, providing it with both aromatic tic and halogenated ether functionliiees, which contribute to ita patential as synthatic intermediate pharmaceu ceuticels agrochemicals. and ma materials science due to its reactivity and structural verselilt.
characterized by hiphenymee thanol
ChatGPT
Query: Delineate te the strucfural Features,
Func tlonal aspects, and applicable implomenta of the matecules \(NAME V. The commance with infraduchonn motecule
2D Net
3D Net
reply
Molecular Describtion
Feature
Feature
Linear
SeiBert
Multi-Head Attention
Linear
Graph Enbedding
Text Embedding
Linear
Attention-based
MLP
Single Step Model Workflow Overview
Product
Single Step
Reactants
Value Fuction
Evaluation Metries
Model
Rank1
SeScore
Prediction Score
RankN
Fine-tuned
Captioning Score
MolIs
F-ine-tuning Process of MolT5
Translation-
OCCLcce(OCCC(CUUCDa
MoLT5
0=C10C(=0) c2cccccc11 Clclceccc1
Product
Reactants
Figure 1: Overview of the RetroInText. A Multiple Step Search Model Workflow of RetrolnText.
Feature Embedding. The product represented molecular graph and geometry features. It is combined with text embeddings generated by ChatGPT and processed through SciBERT for multimodal integration. C Single Step Model Workflow. C.1 fine-tuned MolT5 model generates potential reactants from the product, ranked by C.2 Evaluation Metrics. Reactants are evaluated using ScScore, captioning score, and prediction score to determine synthetic routes' quality and feasibility. D MolT5 transforms the product SMILES into potential reactant structures.

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3.2 RETROSYNTHETIC SCORING METHOD
The goal of retrosynthetic planning is to find a series of reactions that transform the starting material set S C . to the target molecule Mj E M:
M, I I
I= I= (m..... ..... molecule M,, current strategies perform series single-step retrosynthesis predictions by model O-) until all molecules the leaf nodes are from S, form pathways to synthesis Mr, which can be formulated as:
... m,}
M stands for the set of intermediate molecules. Beginning with the target
P {pI, PP, ..., )
where represents the set of pathways to synthesis Mt.
METHODOLOGY
As shown in Figure. our proposed framework RetroInText incorporates a pre-trained moleeu- lar representation model 3DInfomax (Stärk et al. 2022), which is utilized to embed both molecular graph and 3D structural information. ChatGPT-3.5 is applied for generating contextual text informa- tion refers captioning score along the multi-step pathway. Additionally, based mechanism that offers a fusion module to complement these multi-modal representations with in-context learning and fine-tuned language model MolT5 single-step retrosynthesis model.
propose attention-
4.1 RETROSYNTHESIS MODEL.
4.1.1 SINGLE-STEP MODEL
We adopt MolT5 (Edwards al., 2022 as our single-step model O(+). Specifically, MolT5 is a transformer-basee model with an encoder-decoder architecture based on the T5 model, pre-trained on 100 million molecular SMILES as well as C4 dataset which contains 700G textual data. The model is suited to generation tasks such as molecular captioning which have contained wealth of molecular and textual information. However, to adapt the model to our task, we apply a translation- based approach to fine-tune the model. Specifically, shown in Figure. DD, we extract all reactions from the training set and treat the products and reactants in SMILES as two distinct "languages" for translation:
translation products - reactants,
(4)
where products is the intermediate molecule during retrosynthetic planning, while the reactants represent the corresponding reactant molecules. The details of fine-tuning MolT5 model parameters are provided in Appendix A.3] The model is equipped with the capability to handle retrosynthesis tasks. We use it as our single-step model the expansion phase to predict Top-k reactions and their corresponding reactants. The results of the single-step models can be seen in Appendix C.I
4.1.2 MOLECULAR REPRESENTATION
Molecule Graph Encoder. As depicted in Figure. 1 B, we use 3DInfomax as the molecular graph and 3D encoder. The 3DInfomax model consists of a 2D GNN and 3D GNN, utilizing a contrastive learning approach in training. It aligns the molecular graphs with the 3D conformations, maximizing the mutual information between the 2D GNN and the 3D conformation GNN, allowing the model to leverage both molecular structure and 3D conformation information simultaneously. We apply 3DInfomax the selection process to fully utilize both molecular structure and 3D conformation information. The molecule is represented molecule nodes and edges respectively. RetroIn also includes information about the molecule's conformation as 3D cloud points {#1.... [mm} C 3 Then we use 3DInfomax the M Encoder of the graph to get the molecular model:
graph G = (V,E), where and & stands for the set of
Hm = M (nn
(5)
where Hm E Rd, wherein d represents the output dimension of the model and the & corresponds to the graph representation of the intermediate molecules.
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Textual Generator and Encoder. We utilize ChatGPT to generate text. In detail, based the IUPAC names of the products and intermediates, textual description is generated the intermediate molecules along all pathways using ChatGPT. Chemical structures
uniquely represented by IU- PAC names, which are derived from a set of rules mapping structures to linguistic phrases. Chemical structures described by IUPAC names
more natural and language-like like than those described by SMILES, IUPAC names serve as a bridge between chemical molecules and LLMs. Details can be seen in Appendix B We use the following prompt to generate textual descriptions:
Describe the key transition states involved in the synthesis of (products)) | from the intermedi- ates H(intermediates)/. Explain the structural changes and energy barriers for each transition state, and reply to me sentence.
where (produutss) ) corresponds to the IUPAC name of the product, and ((intermediates) corre- sponds the IUPAC names of all intermediate molecules. In instances involving multiple interme- diate molecules, they are concatenated with commas. As shown in Figure. [BB, after obtaining text information from the pathway to the target molecule (41, ,nn SciBERT (Beltagy al.
2019) is used as T Encoder for the textual modal.
H. T Encoder(T).
To ensure no information leakage and to eliminate variations in the textual content generated by ChatGPT in the test phase, we use only the structural information of the product molecules as the textual information for each step. This also ensures that the selected intermediates remain closely aligned with the product molecules. We generate textual descriptions using the following prompts:
Delineate the structural features, functional aspects, and applicable implementations of the molecules (NAME). They commence with the introduction: "The molecule ..." and reply to me in sentence.
where (NAME) corresponds to the IUPAC names the products. SciBERT is also used as encoder for textual information.
Multi-modal Fusion. As shown in Figure. T BB the fusion of molecular and textual representations achieved through attention mechanism. Specifically, while obtaining the molecular representa- tions Hm and textual representations H,, the textual information utilized as the query (Q), while the molecular representations the integration both modalities, allowing for a more effective alignment and interaction between them, thereby enhancing the overall predictive capability of the model.
treated the key (K) and value (V). This approach facilitates
Q = H,W K= Hm HmHA W.
V
Hm W.
QKJ
Atlention = softmax
V.
(8)
Vdl
H, = Attention Q,K, V),
(9)
where Wº E Rdxd,, WK E Rdxds and WV E Rddd are trainable parameters, H, represents
the fused representation.
4.1.3 MODEL TRAINING
The fusion module is integrated into the model's training process, allowing it to effectively handle scenarios where textual information is included in the testing phase:
ÿ MLP(HH).
(10)
Loss 1 Σ yi 91)
(11)
where á represents the model's prediction score, and the model is trained using the Mean Squared Error (MSE) loss.

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4.2 SCORING METHOD FOR GUIDING THE SEARCH
To guide the retrosynthesis search process, we employ a scoring framework that effectively ranks candidate pathways by combining synthetic complexity, reaction costs, and textual alignment. The scoring framework consists of three components: ScScore (Coley et al. 2018). reaction cost score, and captioning score.
Synthetic Complexity and Reaction Cost Scores. The ScScore, ranging from 1 to 5, quantifies molecular complexity while considering synthetic accessibility (Coley et al. 2018). For retrosyn- thesis pathway, the synthetic complexity score Vi is defined in Equation 12, where In denotes the i-th intermediate molecule, and n represents the total number of intermediates. This normalization ensures that lower scores correspond to simpler and more accessible intermediates.
The reaction cost score Vm as defined in Equation 13 evaluates the cumulative cost of reactions within the pathway, where el R2) reflects the reaction cost for R, the reaction producing the inter- mediate molecule. This metric accounts for the feasibility and efficiency of the associated chemical transformations.
The overall pathway score V, defined in Equation 14 combines the synthetic complexity and reac- tion cost scores, prioritizing pathways that are both synthetically accessible and cost-efficient.
SeScore(Zi) - )
V1 Σ
(12)
Vm = Σ c(Ra),
(13)
i=1
= V2 + Vm-
(14)
Captioning Score for Pathway Selection.
In addition the key scoring components, we integrate the captioning score in the selection phase to enhance the selection process. The captioning score leverages textual descriptions generated during retrosynthesis planning to evaluate the alignment between the descriptions of intermediate molecules and the overall pathway context. This alignment provides an additional layer of interpretability and ensures the textual coherence of selected pathways.
For training, ScScore, reaction cost score, and textual alignment are treated as true values, allow- ing the model to learn unified scoring strategy. At inference, the combined scoring framework, including the captioning score, refines pathway ranking by ensuring both chemical feasibility and contextual consistency.
The inference process is summarized in Algorithm | where the scoring framework ranks pathways and guides molecule expansion. This integrated approach enables Retroln to effectively identify retrosynthesis pathways that optimal across multiple dimensions.
Algorithm Retrosynthesis Planning Algorithm
Input: target molecule M,, starting material set S, textual information T Initialize: reactants set R O. path
[M,}
while P is not empty do
Take path p from P. predict reactants I, for expansion given p by O(-)
for reactant I in Ip do
If E S then Put T into R
else
rank p' = ( by computing captioning score of T
put ranked p' into P
end if end for end while
return predicted reactant set R
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5 EXPERIMENTS
5.1 EXPERIMENTAL SETUP
Dataset. As shown in Table. [ uation, which includes 46, 458 molecules as the training set, 5, 803 molecules as the validation set and 5, 838 molecules as the testing dataset. The synthetic pathways for each molecule are extracted from the USTPO-full reaction network. All reactions along the pathways for each molecule in the training and validation set are extracted fine-tune the MolT5 (Edwards al. 2022 model.
the public dataset RetroBench (Liu al. 2023a) for eval-
#Molecules
Depth
4
5
7
8
9
10
11
12
13
Datasets
Training
22,903
12,004
5,849
3,268
1,432
594
276
107
25
0
0 0
Validation
2,862
1,500
731
408
179
74
34
13
0
0
Test
2,862
1,500
731
408
179
74
34
13
2
32
2
1
Table Statistics of molecules at various depths summarized from the RetroBench dataset.
Baselines. Retrosynthetic planning strategies integrate retrosynthesis models with search algo- rithms. We compare our model with template-basee models, including Retrosim (Coley et al. 2017), Neuralsym (Segler & Waller 2017) and GLN (Dai et al. 2019). We also compare with template-free models, such as Transformer (Karpov et al. 2019) Megan (Sacha et al.) (021) and FusionRetro (Liu et al.) (023a),
well semi-template- based models, including G2Gs (Shi al., 2020) and GraphRetro (Somnath et al. 2021). Additionally, we compare RetroInText with Fu- StonRetro+CREBM (Liu et al.) (0246) that incorporate energy functions for reranking. In detail, CREBM is framework that enhances molecule synthesis by integrating energy functions to eval- uate and rerank synthetic routes, thereby improving the quality of the generated pathways. Upon completion of the retrosynthesis training, we employ the first **-like algorithm guided AND-OR tree search methods Retro* (Chen et al. 2020), Retro*-0, which is indeed a beam search algorithm, and Greedy DFS search algorithms.
Evaluation Metries. We utilize the commonly employed evaluation performance metrics Top-k (k: 1,2, 3, 5) exact match accuracy to evaluate the retrosynthesis performance proposed by Liu et al. (2023a). The exact match accuracy computed by comparing predicted reactants SMILES to the dataset's ground truth on the benchmark dataset. More experimental setups can be found in the Appendix A
5.2 RESULTS
Comparison with Baselines. The performance of all methods is presented in Table. 2 Compared with all template-free models and the reranked CREAM model and the State-Of-The- Art(STA) model FusitonRetro (Liu et al., 2023a), ceeding the Top-1 accuracy of FusionRetro with CREBM by 1.8%, achieving SOTA performance. RetrolnText also demonstrates superior performance across different search algorithms, even ap- proaching the top results of template-based methods with Retro*-0 and Greedy DFS, highlighting the benefits of using LLM and route description.
model Retroln achieves the best performance,
Analysis for the Depth of Routes. To better evaluate the performance of our proposed model across varying levels of retrosynthetic complexity. we analyze the prediction accuracy different depths using Greedy DFS, shown in Figure. 2 Our model Retroln demonstrates competitive performance across different depths, particularly excelling in longer synthesis routes. Compared to other baselines, our model maintains more stable decline with increasing depth. While models like GraphRetro and Megan sharply drop beyond depth 4, RetrolnText retains a significant margin, demonstrating robustness and effectiveness in deeper, more complex retrosynthetic planning.
Ablation Experiments. To better understand the contribution of each component within our pro- posed framework, conduct series of ablation experiments. As shown in Table. our model Tex,, consistently outperforms the baseline model across all Top-N accuracy metrics, demon- strating the effectiveness of proposed enhancements. For instance, in terms of Top-1 aceu-
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Scarch Algorithm
Retro
Rctro+-0
Greedy DFS
Single-step Models
Top-1 Top-2 Top-3
Top.4
Top-5
Top-1
Top-2
Top-3 Tup-4 Top-5
Top-1
Template- based
Retrosim Coley al. 2017
35.1
40.5
42.9
44.0
44.6
35.0
40.5
44.1
31.5
Neuralsym Segler Waller 2017
41.7
49.2
52.1
53.6
54.4
42.0
49.3
52.0
53.6
54.3
39.2
GLN (Dai al.
2019
39.6
48.9
52.7
54.6
55.7
39.5
48.7
52.6
54.5
55.6
38.0
Semi-template-bage
G2Gs SSi al. et. 2020)
5,4
8.3
9.9
10.9
11.7
4.2
6.5
7.6
8.3
8.9
3.8
GraphRetro (Somnath al.
2021
15.3
19.5
21.0
21.9
22.4
15.3
19.5
21.0
21.9
22.2
14.4
GraphRett=CREEMM [Luu et al.
2024b
20.1
21.6
22.3
22.7
16.3
20.2
21.6
22.7
Template-free
Transformer Karpov et al.
2019
31.3
40.4
44.7
47.2
48.9
31.2
40.5
45.1
47.3
48.7
26.7
Monntfomer+CREBBl
35.0
43.4
46.7
48.7
49.7
34.0
43.1
46.4
48.3
49.4
Megan (Sacha et al. 2021
18.8
27.9
32.7
36,6
38.1
18.6
27.7
32.6
36.4
38.5
32,9
FusionRetro [iu al.
2023a
37.5
45.0
48.3
50.6
51.5
37.4
45.0
48.4
50.4
51.1
35.2
FusionRetro+CREBBB.
Liu ct al.
2024b
39.4
46.6
49.3
50.7
51.5
39.6
46.7
49.5
51.0
51.7
33.8
RetroInText (Ours)
41.2
48.7
51.8
53.3
54.2
42.1
49.9
53.0
54.7
55.7
39.8
Table 2: Summary of retrosynthetic planning results for exact match accuracy (%).
racy, Rexroln'Text achieves 4.0% increase MolT5 Similarly, compared RetroIn- Text(Graph), where we test using FusionRetro (Liu et al. 2023a), which achieves 37.5%. RetroIn- Text shows 3. 3.7% improvement. These results suggest that the synergy between structural features and text-aware components substantially enhances predictive accuracy. Additionally, the removal of the textual component, as indicated by the RetroInTextt (w/o text) configuration, results in a Top-1 accuracy of 40.2%. Compared to the complete RetrolnText model, which achieves 41.2%, high- lighting the value the textual module in providing essential contextual information that supports more accurate predictions. Further details on the analyses and experimental setup can be found in Appendix C which provides additional insights into the significance of each module.
Greedy DFS
GraphBettr
Neurliga
RetrolTestt
0.4
0.3
Accuracy
6
Depth
Figure 2:
Test accuracy of retrosynthesis models combined with Greedy DFS at different depths. The red star stands for our method RetroInText.
Additionally, we conduct experiments across different depths, which demonstrate that incorporating textual information consistently improves performance at all levels. As shown in Table. Retro* outperforms Retro*(wi)) (w/o text), particularly at increasing depths, showing robustness in predicting long synthetic routes. The most pronounced gains in Top-1 Top-5 accuracy occur at deeper paths

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Published as a conference paper at ICLR 2025
(Depths 5 to 8), highlighting the effectiveness of textual data in enhancing prediction accuracy for complex retrosynthetic planning tasks.
Methods
Top-1
Top-2
Top-3
Top-4
Top-5
MoIT5 (SMILES)
37.2
43.7
46.2
47.4
48.3
RetroInTexx(11D SMILES)
35.6
41.6
45.4
46.2
(etroInTex((2+33) Graph)
37.5
45.0
48.2
50.0
50.9
CeroonTText((e text)
40.2
47.3
50.2
51.7
52.7
RetroInText
41.2
48.7
518
53.3
54,2
Table 3: Ablation study of RetrolnTexxt for exact match accuracy (%).
Retr text)
Retro*(with text)
Depth
Top-1
Top-2
Top-3
Top-4
Top-5
Top-1
Top-2
Top-3
Top-4
Top-5
Depth2
45.0
52.4
55.4
57.2
58.3
44.9
52.3
55.4
57.3
58.3
Depth3
38.9
45.9
49.3
50.5
51.5
40.0
47.9
51.5
53.0
53.9
Depth4
33.7
40.9
42.5
43.6
43.6
36.1
43.6
46.4
47.7
48.3
Depth5
35.5
41.7
43.4
44.4
44.4
39.0
47.8
50.3
51.2
51.7
Depth6
36.9
38.0
38.0
41.9
43.0
44.1
Depth7
25.7
31.1
31.1
31.1
31.1
28.4
33.8
35.1
35.1
35.1
Depth8
29.4
41.2
41.2
41.2
41.2
32.4
41.2
44.1
47.1
47.1
Table 4: Exact match accuracy (%) at different depths of ground truth synthetic routes.
CONCLUSIONS
In this work, propose RetrolnText, novel framework for retrosynthetic planning that leverages contextual information along synthetic route through ChatGPT. RetroInText employs in-context learning incorporate textual information from previous steps, enhancing realistic retrosynthetic planning. Additionally, we use fine-tuned language model, MolT5 (Edwards et al.) 2022). along with pre-trained molecular representation model to integrate both molecular structure and 3D con- formational data, improving the selection process. Experiments on the RetroBench dataset demon- strate that Retroln'exxt outperforms existing template-free methods, achieving SOTA performance. Further experiments at various depths and ablation studies show the strength of text information during retrosynthetic planning. In the future, answering model (Maziarz et al., 20222] Liu et al.) 2023d) to further improve retrosynthetic step selection and enhance the utility of a deep yearning-basee based retrosynthesis model.
planning to develop an end-to-end question-
ACKNOWLEDGMENTS
This study was supported by grants from the National Natural Science Foundation of China (NSFC 62322215). This study was supported in part by the high-performance computing center of Central South University.
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A EXPERIMENTAL PROCEDURE AND SETUP
A.1 EVALUATION METRIC
The current search algorithms (Segler ct al. 2018 Chen ct al. 2020 Kim et al. 2021 Yu ct al. 2022 Obonyo et al. 2023 Yuan et al. 2024; Xic et al. 2024) predominantly rely search success rate as the primary evaluation metric, without assessing whether the identified intermediates are capable of synthesizing the target molecules. As illustrated in Figure. B) integrating existing one-step models, which achieve top-k accuracies in the range of 60% to 80%, with the Retro algorithm, we observe that the search success rates for the multi-step retrosynthesis process increase between 85% and 94% (Liu et al. 2023a). This outcome appears counterintuitive, as one might expect decrease in success rate with the addition of each synthesis step. Consequently, we adopt the new evaluation metric proposed by FusionRetro, which considers the set of precise matches between the predicted materials and the baseline reference. prediction deemed correct when the set of actual materials obtained from the model matches at least one of the feasible synthesis routes in the target molecule set. Furthermore, a search for paper cuttings is conducted. The search terminated when the length of the predicted synthesis path exceeds the depth of the true synthesis path. We use the starting materials derived from the reaction network in RetroBench as the baseline and compare them with the starting materials identified through the proposed search process.
USPTO
100
Retroeynthesie Prediction
Retrosynthetic Planning Search
94.5
92.9
Top-5 Accuracy & Success Rate (%)
86.0
78.0
00.8
GraphRetro
Retrosim
Neuralaym
Figure 3: Performance of different retrosynthesis models for retrosynthesis prediction and multi-step planning on USPTO dataset. This result has been reproduced from FusionRetro (Liu et al. 2023a).
A.2 IMPLEMENTATION DETAILS
We use Pytorch Paszke et al., 2019) to implement our models. The codes baselines are implemented referring the implementations of FusionRetro (Liu al. 2023a) and CREBM (Liu et al. 2024b). All the experiments of baselines are conducted on a single NVIDIA 4090 with 24GB memory size. The softwares that 0.7.0, pytorch 2.2.0, pytorch-scatter 2.1.2, pytorch-sparse 0.6.18, numpy 1.24.4, torchvision 0.17.0. The total inference time is 79.5 hours.
for experiments are Python 3.8.19, CUDA 11.5.119, einops
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A.3 FINE-TUNE PROCESS
All reaction in the training set is extracted as the fine-tuned dataset which includes more than 150K reactions. We train it for 40 epochs and choose the best checkpoint single-step model. The detailed training parameters are shown in Table.
Parameter
Value
Description
Learning rate
10 5
Step size for optimization
Batch size
8
Number of samples per batch
Epochs
40
Number training iterations
Hidden layers
24
Number of layers in the model
Hidden units
768
Number of neurons per layer
Head number
12
Number of multi-head per layer
Save steps
5000
Save checkpoint step Beam search number
Beam number
4
Weight decay
0.1
Regularization coefficient
Table 5: Fine-tune parameters.
B MOLECULE NAME GENERATION
Before using ChatGPT to generate the text information for the routes, should get the IUPAC names as mentioned in Section 4.1.22 Specifically, we extract all the intermediates with the matched depth in the training set for all products then generate products and corresponding intermediate IUPAC names by PubChemPy. For the test set, we generate the IUPAC names of the products, but we only need them during creation.
C MORE RESULTS
C.1 SINGLE-STEP MODEL. RESULTS
All the reactions in the test dataset are extracted for evaluating single-step models, with an overall
24, 972 reactions. The results are shown in Table.
Top-k accuracy (%)
Models
Top-1
Top-3
Top-5
Top- 10
ResoonRetro
31.1
39.4
42.3
47.0
Transformer
28.1
38.7
41.8
46.0
MolT5-small
20.8
30.0
33.9
38.3
MolT5-basc(Ours)
33.3
39,9
42.1
44.5
Table 6: Retrosynthesis prediction results for exact match accuracy (%).
C.2 PROMPT IMPACT PATHWAY DESCRIPTIONS
To test the impact of text quality the model, specifically, two distinct prompts were evalu- ated, as outlined in Table. to assess their impact on the textual descriptions of the molecule O-CIN-CCOOCCIN--CCCC- CCce:oooCCCCCecceCCCCCCCCCCCCCA
The first prompt is designed to generate detailed, yet excessively verbose descriptions, while the second prompt focuses on producing con- cise and targeted textual outputs.
Prompt1 is characterized by a lack of specificity, resulting in verbose descriptions with considerable redundancy. These excessive details, which include irrelevant information, have detrimental effect on the model's accuracy. In contrast, Prompt2 is deliberately designed to prioritize clarity and relevance, avoiding superfluous content. The generated text is both concise and well-structured,
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contributing to enhancement in downstream model performance. As illustrated in Table. & the use of text generated from the higher- quality prompt leads to a notable improvement in the performance of the framework. Specifically, the Top-1 accuracy increases from 39.2% to 41.2%, while the T accuracy improves from 46.9% to 54.2%. These results underscore the significance of carefully crafting prompts to generate concise, high-quality text in order to achieve better model predictions.
Target Molecule
Prompt
Text
o=ciN=c (o) CN
Please deserite the structural teatures, fonciional and replicable im- prodees doaail the meliate molecules intermediates the symthesis pathway. ase molecule seporated by "SUPP": ditotally, provide a difierent reaction pullway and doserihe
The molecule c/4-chloo--3-0 tww.-/iinooeeetllylllll [pheuyl] N- (2,4-lookiiiaaedlll I 1ylpmpp- 2-enumide complex
1N-C C-Celcoc
compondd that festunos saboinste phanr! imisazolddin yl and etamide functional group. Structural features: Subatilueed pleas) group chhkeine grups. Prop--cnamil funciionl group. Funcsional riag sabatinted phenpl propp provides peamuticity and patetially peete
(C (P)
mettods of molecule inter-
tiffurnacclyy proup 2. Iniiluediiii
the
molecule's reactiiity and solabiiity The inidazolidia yt ring may paracipote driges banding insenacionn
peop 2 esomide functional group can patticigate is conjugation reactions or undego mecosphilic uddition reacioon. Applicable implementaion mothods: Synthees of molemuls may involve varloss organic chemistry techniques such as Grignand reactions amide band formation, and eyclizazion reactions Parificttes teciniques such as columo diremsingraphy, recrystal lintion, or diviilatios may be used to dbtin dhe findl product in high purly. Poteicial incemedite molecales: I. J-fd-chlom- (oom----tin highihoeeeelllghhh//llll patpyyllpppp-eeee--e mmp-2-encic acid. Dif- formt nastion pathway Insterd of directly synthoosing the orpet malerall, an altemnative pathway might involve the initial formation 3 [4 chioco 3 triifoormeeittllpeeeee Z-enoic acid in intermoliate. Sulsequeaalyy the pmp-2-eteii acid cus be cosvertal the desined prop-2-enamide thruuhh amide
motery stabilizztion
reaction with the iniduzolitin- lyl Overall, the syrthesis of such comples molocalss rquiress coroful platning, knowel edge of oegatic chemistry reactions, peoper chamcterizziinn techniques contimm the insemediatic final products.
O-C1N-C (0) CN (0) C=Celese
Describe transition
mediat trassiion states involved in syathesis include the formation of inine conkusation prop---nnie and the group, fellowad by cyclizaion inine inidazlldia---91 ring.
subsal the synthsis proluntt dhe intraeliiiss incemediates. Explann enettral changes meny as each canstion sule, and reply to me sentence.
(C (P) cl
Table 7: Comparison of Prompts and Textual Descriptions.
Model
Top-1
Top-2
Top-3
Top-4
Top-5
RetroInTexxt (Prompt1)
39.2
43.4
45.6
45.7
46.9
RetroInText (Prompt2)
41.2
48.7
51.2
53.3
54.2
Table 8: Comparison of prompt influence on textual quality for molecular descriptions and reactions
Target Molecule
Text (ChatGPT-3355
Text (ChatGPPT-I)
Similarity
cc (C) Nel acccee2222
the synthesis: key transition states involved conversion of chioo-- N propan propan-2 2-ytthienol 3,5 d'pyrimidiin 4 amine to N-propnn2 2 2-yhhiennf]3 4 4JIppyimddi 4-amine, with the sinctural chinge being by comversion of to
syotesis of N-propas- N-propaa-2-ytthienntts 2-ythhinott dlpytimiii- a-mme involves key trnsition sttees that typically include nucleophilli subainuionn where the chiorine atom in Z-chloro--ppee -aroouuo----rpn--e-- prsppp----llliiinne (itieninoo]] (ihieen(]AA-J]rrrinnn 4ryrriiiiddd-- group. with
These by different models deserbe the same key transition states, including the na- ckophilic subsitution where the chiorine
dispacceent of the chiorine amine group propan- myppnn2-ltthienn3... 4-dlyyrrriiiiiiiiii- N-prpan-2-ythee 2-ylthinno 1,4 1,4-1gyrimiiiin d-dlyyrimiii---ammnn with the strictura change being the fomtttio of the final amine functionality: both trancitions evegy hurriers reactions prossat.
amine replaced an trancition state requinng the alignment the nuckophile and electrophile, ovmrooming the energy harrier intiibated in brraking the C-C1 boad and forming the new C-N hand, uvnally churacterized by partially formed bond and high-mnergy. intrahedial intermediate.
replased by an amine group and a tautomeric shin. Roth mention energy berriers involved the tramitiinn states.
CN-C (0) NCC.Culsse Enld] ml The transition
involved in symthess of 1-metty-33(2221H (1a-psaazo--.. ylmedbylamino)sshlll
In synthesis 1-methyl---12-118 22-12-1H-pyyazal-5 yimethylamino lethyllurea from (HH-pprzzol- yymeehylletie Dethane 1.2 diamine, the key trans- dim stoses include the nucleophills attnck of the amine on the isceyunate earben to form tetrahe- seitenonic internediatt, followed pro- ton transfer to establish unethane linkage an energy barrier influenced by sterios and olee- tronic effeets, leading to the final ura product.
The mechanisms and transformations described in both are similar, involvigg the formation of the urea bund and methylation.
from the inserme- disee N1l(Il-pyruud ---ylmeyllleehmmm 1,2- diamine inclate the formation of a urea linkage and additin of methyl group. These transitions involve structural reamangemenss eneryy barriers that facilittt the convenion of the intemmediatss into the peditedpendacct.
Table 9: Comparison of textual descriptions generated by ChatGPT-3.5 and ChatGPT-4
C.3 COMPARISON OF OUTPUTS BETWEEN GPT-3.5 AND GPT-4
We employ GPT-4 for text generation, and comparison between the outputs generated by GPT-4 and GPT-3.5 reveals a high degree of similarity in both content and the structural transformations described, as illustrated by the examples provided in Table. 9 Specifically, both models effectively
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Published as a conference paper at ICLR 2025
characterize key transition states for the target molecules, including nucleophilic substitutions, tau- tomeric shifts, and the associated energy barriers for bond-breaking and bond-forming processes. However, GPT-4 presents certain practical challenges, particularly with frequent API key limita- tions, which disrupt the workflow and diminish its reliability for consistent use. In contrast, GPT- 3.5 exhibits stable performance without such restrictions, making it more dependable choice for our framework. Given the negligible differences in performance and the operational constraints of GPT-4, GPT-3.5 is selected the primary text generator for the experiments.
C.4 ABLATION STUDY
To test the role of each part in the framework, perform the ablation study RetroInText. First, we use the no fine-tuning MolT5 model as the single-step model and observe that the generated SMILES strings for the corresponding molecules invalid, resulting in scores of 0 across all cases. This indicates that the original MolT5 model not suitable for our task, and fine-tuning is necessary.
We also experimented only using the combination of SMILES and text to train the model, however, this combination is inferior those of multimodal approach. Next, as the single-step model, without incorporating the molecular representation model or textual infor- mation, and only rely on molecule fingerprints for scoring. Finally, introduce textual information into the training process and use 3DInfomax as the molecular representation model, while exclud- ing textual information in testing. The results demonstrate a significant improvement in multi-step accuracy when textual context information is included, indicating that using textual information in multi-step processes is highly effective. As shown Figure. the case of depth3 shows text can make accurate predictions compared to not using text.
the fine-tuned MolT5
Prodiction Text
The anlacule sthyl cyanenen enoate compound with chenical steucture consisting of ethyt cyanide and 9-cyanenon -2-enoate nolety or group.
Fanli: 9.6679
rank2: 0.8579
Text
B.1812
Figure 4: Comparison of retrosynthesis prediction with text (A) and without text (B).
18


Reviews:
[{"review_id": "hzuOtBhyZX", "reviewer": "Reviewer_zdz7", "summary": "The authors target a more realistic retrosynthesis scenario, considering multi-step synthesis and cost function. Solving retrosynthesis planning and multi-step reasoning about the intermediate is important, which current LLM-based methods overlook. To tackle this, the authors suggest an E2E framework involving learnable multimodal LLM utilizing a 3D molecule encoder and (possibly closed source) LLM generating text descriptions for intermediate synthesis steps. By fusing the multimodal information via the proposed attention mechanism and leveraging LLM generated reaction descriptions, the proposed method outperformed semi-template-based and template-free SOTA methods and recorded comparable performance to the SOTA template-based method in RetroBench.", "questions": "- In section 4.2, Textual Generator and Encoder, there is a confusing explanation related to the use of textual description. The authors used textual descriptions of the intermediate molecules along all pathways but, in test time, only used the text description of the product molecules to prevent information leakage, which is a train-test mismatch. But why is this data leakage, given that ChatGPT is not trained during training time?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- The authors proposed a principled way to leverage LLM in retrosynthesis using multiple modalities, including molecular structure (2D graph and 3D conformation) and text descriptions of chemical reactions.\n- The authors' method of generating text descriptions and ranking candidate reactants for multi-step retrosynthesis would inspire many future researches.\n- Until now, LLM-based retrosynthesis methods have primarily focused on single-step retrosynthesis, leaving few studies available for the potentially more significant multi-step retrosynthesis in the domain. Additionally, they have not considered the reaction cost, which is crucial in real-world reaction pathways. By conducting multi-step retrosynthesis based on a value function that takes into account a cost function, the authors have narrowed the gap between LLM-based retrosynthesis and real-world applications.\n- The proposed method outperformed not only semi-template-based and template-free methods but also most template-based methods. This is interesting because the performance is achieved largely based on LLM’s generalization via ICL and reasoning ability, without relying on inductive bias, including reaction template, so with further future potential.", "weaknesses": "- The motivation to maximize mutual information of 2D graph and 3D conformation is not sufficiently presented. If one can use 3D conformation, which is generally believed to have more information than a 2D graph, what is additional information from the 2D graph? It seems that Table 3 does not resolve this matter.\n- It is necessary to add the ablation of 1D molecular sequence substituting graph embedding to demonstrate the supremacy of spatial representation, considering that the molecular sequence is generally regarded as a more advantageous modality than 2D graph and 3D conformation. Could you add the ablation study?\n- I wonder how much the proposed framework is sensitive to the quality of textual description and what quality control process would help (GPT-3.5 could be quite old these days for SOTA-level performance achievement), which I found no available analysis related. Could you provide an analysis related to this matter?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "eSTw8eHb2j", "reviewer": "Reviewer_xJUs", "summary": "The paper introduces RetroInText, a multimodal framework for retrosynthetic planning, enhanced by in-context learning with large language models. RetroInText leverages textual descriptions generated by ChatGPT and molecular 3D and graph-based representations to improve retrosynthesis prediction accuracy.  This framework leverages detailed textual descriptions of reaction procedures generated by ChatGPT, combined with molecular graph and 3D features, to create multimodal representations that better capture the complex relationships between molecules and synthetic routes. The model incorporates an attention-based fusion mechanism that integrates these multimodal representations, enabling it to learn from both molecular structure and contextual information. By fine-tuning a MolT5 for single-step retrosynthesis, RetroInText adapts to the intricate nature of retrosynthesis tasks. On the USPTO pathways dataset, RetroInText achieves up to a 5% improvement in Top-1 test accuracy over state-of-the-art methods", "questions": "Here are the questions for the RetroInText paper:\n1. Could the authors explain the difference in Retro* and Retro*-0 search algorithms? This was not provided in the text.\n2. For the results table provided no uncertainty/error is provided in the overall performance. This makes the comparison of numbers quite hard. Especially given the fact that most attention based models' performance change quite a bit between different random seeds. Would the authors provide experimental repeats and with that present standard deviation of the performance to better compare RetroInText to other baselines?\n3. Please provide the detail on the compute power and the time it takes for training and inference of RetroInContext compared to other baselines.\n4. ChatGPT is used to generate textual descriptions, yet these descriptions may lack specificity and relevance for retrosynthetic tasks. Could the authors provide more detail on why they selected ChatGPT over domain-specific LLMs or fine-tuning a language model on chemistry-specific corpora? \n5. Would the authors consider adding metrics that assess the interpretability of predictions or evaluate the predicted intermediates' consistency with known chemistry principles?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 4, "contribution": 2, "strengths": "The paper has the following strengths:\n1. The paper’s integration of molecular graph, 3D structure, and in-context textual data provides a comprehensive approach to retrosynthetic planning. This combination allows RetroInText to capture the complexity of chemical synthesis from multiple perspectives, marking an original approach in retrosynthesis where LLMs are used to contextualize synthetic routes. This multimodal approach demonstrates high methodological quality, even though the performance gains are limited. By introducing this multimodal approach, the paper shows potential significance for future studies aiming to integrate similar techniques in other fields.\n2. Using ChatGPT to generate in-context textual descriptions based on IUPAC names and structural features is an approach that enriches retrosynthetic planning with additional chemical insights. This originality suggests potential for further exploration in LLM-assisted retrosynthesis. Although the actual significance may be limited by the gains achieved, the concept of using LLMs to add synthetic context could inspire similar uses in adjacent fields.\n3. RetroInText employs an attention-based fusion module that effectively combines textual and structural molecular representations. This fusion step is crucial for allowing the model to weigh and integrate these varied inputs. In terms of quality, the fusion module is a well-executed method for ensuring that multimodal data can interact meaningfully, enhancing the model’s potential to capture complex dependencies between chemical steps. The mechanism is clearly described, adding to the clarity of the methodology, though the actual impact on performance is modest.\n4. The paper’s adaptation of MolT5, a language model originally designed for SMILES-based tasks, as a single-step retrosynthesis predictor is a clever methodological choice. Treating the retrosynthesis task as a translation problem aligns well with the model’s strengths, bringing originality to how language models are applied in chemistry. This choice also reflects quality in adapting existing models to new domains. However, while the approach is interesting, the overall improvement in prediction accuracy suggests that this strength is more exploratory than transformative.\n5. The paper provides a good assessment of the contributions of each module through ablation studies, helping to clarify the unique roles of textual and structural data in the model. This thorough evaluation approach enhances clarity, making it easier to understand the marginal improvements contributed by each component. The methodological rigor shown here is a positive aspect of the paper’s quality, even if the results themselves do not indicate a significant practical advancement.", "weaknesses": "The paper suffers from the following weaknesses:\n\n1. While RetroInText introduces a multimodal framework, the performance gains it achieves over baseline models are relatively modest, with only a 5% improvement in Top-1 accuracy on the RetroBench dataset. This incremental improvement does not fully justify the complexity introduced by the multimodal integration of text, graph, and 3D structure data. To enhance the significance and novelty of this work, the authors could focus on refining specific modules, such as further tuning the MolT5 model or the fusion mechanism, to achieve more substantial performance gains.\n2. One area that is not explored by the paper but feels lacking is that the paper is using GPT 3.5. Compared to more recent models (GPT-4o, Llama 3) the performance of GPT 3.5 is limited especially in science domains. Although in-context textual descriptions generated by ChatGPT add an innovative dimension, they may not offer significant chemical insights specific to retrosynthesis tasks compared to the newer models. Additionally, the generic nature of these descriptions could lead to data that does not substantially improve the model’s understanding of chemical reactions. Therefore, experiments for comparison when a less generic description is provided by more recent LLMs is missing that could shed light on the effectiveness of the methodology. Also a more effective approach could also involve fine-tuning open-source LLMs or using a chemistry-specific language model trained on domain-specific texts to produce text that better captures the critical reaction features.\n3. While the attention-based fusion module is a central part of RetroInText, the authors do not clearly justify why it is the optimal choice for combining multimodal data in retrosynthesis. The paper could be improved by a deeper analysis of the fusion mechanism, potentially comparing it with alternative fusion strategies (e.g., cross-attention or co-attention modules). Such an analysis would add clarity and help readers understand whether the observed performance gains are due to the multimodal data itself or the specific fusion approach chosen.\n4. The evaluation focuses primarily on Top-k accuracy metrics, without providing chemical interpretability insights or explaining how the model might aid chemists in real-world settings. Retrosynthesis models are most useful when their predictions can be understood and trusted by chemists, yet the current metrics do not assess interpretability. Including an analysis of the quality of the predicted intermediates or comparing predicted synthetic pathways with experimentally validated routes would strengthen the paper’s practical relevance and significance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "k8b07XpJoH", "reviewer": "Reviewer_cGGk", "summary": "The paper focuses on retrosynthesis analysis of molecules using large language models (LLMs) such as ChatGPT. It employs ChatGPT to generate reaction descriptions, then incorporates both text and structural information of the product molecules into another model, MolT5, to suggest potential reactants. The A* algorithm is then used to search for synthesis routes. This work addresses an important problem with an interesting approach, capable of generating reaction descriptions alongside reaction formulas, potentially improving interpretability in retrosynthesis analysis. The paper could benefit from improvements in the discussion of its motivation, methods, and experiments.", "questions": "1. What is the Retro*-0 algorithm?\n\n2. How long does it take for the proposed framework to find a solution for a single molecule? Is it significantly longer than the baseline, given the many models involved in Figure 1?", "limitations": "", "rating": 3, "confidence": 4, "soundness": 1, "presentation": 1, "contribution": 2, "strengths": "1. **Originality**: The idea of using LLMs to generate text descriptions for retrosynthesis is interesting.\n2. **Quality**: The paper presents a generally reasonable framework.\n3. **Clarity**: The paper includes figures and algorithms to aid readability.\n4. **Significance**: The paper presents experiments with promising results.", "weaknesses": "**Originality**: The motivation of the paper needs further clarification from different perspectives:\n\n1. Lines 44-46: Why are graph and SMILES representations limited in capturing the complexity of chemical structures? How does the major focus of this paper—text—help solve these issues? This conclusion is counterintuitive, as many recent studies have shown that graph- or SMILES-based models often outperform even the most advanced text-only models, such as LLMs.\n\n2. Lines 51-53, 66-68: Throughout the paper, it is unclear how the proposed idea addresses these issues.\n\n**Quality**: The proposed framework needs more justification.\n\n1. Lines 69-78: There is no clear connection between the proposed framework and the previous paragraphs. The idea of using \"ChatGPT (Edwards et al., 2022) to present detailed descriptions of the reaction procedure\" is bold and lacks justification. Can ChatGPT truly generate accurate reaction procedures? How reliable is it, and how can errors be prevented from misleading or disrupting the framework?\n\n2. Some claims are incorrect. Line 479/Figure 1: MolT5 is not an LLM.\n\n**Clarity**: The paper’s writing needs improvement.\n\n1. Please check all citations. For example, the citation for ChatGPT on line 70 is incorrect, and the citation for ScScore (line 75) is missing.\n\n**Significance**: The results presented in the paper need more justification.\n\n1. The experimental setup is unclear. For instance, what are the specifications of the A* algorithm (e.g., search iterations, candidates, time)? What are the building blocks? \n\n2. The paper lacks case studies and systematic evaluations on the quality of reaction descriptions generated by ChatGPT.\n\n3. Regarding the evaluation metrics, a single molecule can have multiple potential synthesis routes. How does the exact match metric comprehensively evaluate model performance if the model suggests a route different from the one in the dataset, yet still a useful route?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "3yUwdLu5IN", "reviewer": "Reviewer_BszM", "summary": "This paper does two main things:\n\n1. For retrosynthesis prediction, it fine-tunes MolT5 specifically for one-step retrosynthesis, aiming to improve accuracy in predicting the immediate precursor of a target molecule.\n\n2. For the search algorithm, it introduces a synthetic complexity score, reaction cost (calculated as the negative log likelihood of a retrosynthesis prediction), and the captioning score to identify the most promising reactants. The prediction score, based on the three types of scores, helps prioritize reactants based on their likelihood of successful synthesis. To learn the prediction score effectively, the paper proposes a multimodal fusion module that combines 3D molecular conformation data and textual descriptions, producing a comprehensive molecular embedding that enhances learning.\n\nExtensive experimental results demonstrate the superior performance of this proposed approach.", "questions": "Could this score be used as a reward to train a residual energy-based model [1] for reranking purposes?\n\n[1] Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models, ICML 2024", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. It’s impressive to see this paper employ set-wise matching between the predicted and reference starting materials in RetroBench's test dataset rather than relying on the search success rate. This metric is both more accurate and powerful, providing a true reflection of the model’s and search algorithm’s performance in practical multi-step retrosynthesis scenarios. Notably, this approach achieves state-of-the-art (SOTA) performance among template-free models.\n\n2. The paper’s approach to fine-tuning a pretrained model on the retrosynthesis task is commendable. To the best of my knowledge, few works have leveraged existing pretrained models specifically for retrosynthesis, highlighting the potential for further exploration into how large molecule models can be pretrained and how foundation models can be fine-tuned for retrosynthesis. This paper suggests that a molecule foundation model, rather than a language foundation model, might be better suited for chemistry-focused tasks, a valuable insight for future research.\n\n3. Additionally, the paper proposes a robust search algorithm that uses three key scores—synthetic complexity, reaction cost, and captioning score—to identify the most promising reactants during the search process. Furthermore, it incorporates both 3D structural data and textual information to enhance predictions. Though complex, this approach proves highly effective and could inspire future work to explore additional scoring metrics for search algorithms.", "weaknesses": "1. The writing could be improved for clarity. First, Figure 1 lacks sharpness; I recommend replacing it with a clearer vector diagram for better visualization. Additionally, restructuring the paper into two distinct sections—one for the retrosynthesis model and another for the proposed scoring method for guiding the search—would enhance readability and help readers better understand the key contributions.\n\n2. The training process for the prediction score is not sufficiently explained. Equation 8c references only two score types: synthetic complexity score and reaction cost. However, Figure 1 also includes a captioning score, making it unclear how all three scores are utilized within the search process. Clarifying the use of these scores would improve understanding.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)