OSDA Agent: Leveraging Large Language Models for De Novo Design of Organic Structure Directing Agents
Authors: Zhaolin Hu
Yixiao Zhou
Zhongan Wang
Xin Li
Weimin Yang
Hehe Fan
Yi Yang
Venue: ICLR 2025 Spotlight (2025)
Keywords: Keywords: Large Language Model
OSDA
Zeolite
Molecular Design
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: Zeolites are crystalline porous materials that have been widely utilized in petrochemical industries as well as sustainable chemistry areas. Synthesis of zeolites often requires small molecules termed Organic Structure Directing Agents (OSDAs), which are critical in forming the porous structure. Molecule generation models can aid the design of OSDAs, but they are limited by single functionality and lack of interactivity. Meanwhile, large language models (LLMs) such as GPT-4, as general-purpose artificial intelligence systems, excel in instruction comprehension, logical reasoning, and interactive communication. However, LLMs lack in-depth chemistry knowledge and first-principle computation capabilities, resulting in uncontrollable outcomes even after fine-tuning. In this paper, we propose OSDA Agent, an interactive OSDA design framework that leverages LLMs as the brain, coupled with computational chemistry tools. The OSDA Agent consists of three main components: the Actor, responsible for generating potential OSDA structures; the Evaluator, which assesses and scores the generated OSDAs using computational chemistry tools; and the Self-reflector, which produces reflective summaries based on the Evaluator's feedback to refine the Actor's subsequent outputs. Experiments on representative zeolite frameworks show the generation-evaluation-reflection-refinement workflow can perform de novo design of OSDAs with superior generation quality than the pure LLM model, generating candidates consistent with experimentally validated OSDAs and optimizing known OSDAs.

Published as a conference paper at ICLR 2025
LEVERAGING LARGE LANGUAGE MODELS FOR DE Novo DESIGN OF ORGANIC STRUC- TURE DIRECTING AGENTS
OSDA AGENT:
Zhaolin Hut.2, Yixiao Zhou Zhongan Wang", Xin Li 4x ,Weimin Yang* Hehe Fan?, Yi Yangl.
The State Key Laboratory of Brain-Machine Intelligence, Zhejiang University, China 2CCAI, Zhejiang University, China SShanghai Innovation Institute, China The State Key Laboratory of Green Chemical Engineering and Industrial Catalysis, Sinopec Shanghai Research Institute of Petrochemical Technology, China {12321165, 1242 122211881 zhon anwang, hehefan, yangyics } ju. edu en { {lixin. sshy, yangwm. sshy e siisiii........ com
ABSTRACT
Zeolites are crystalline porous materials that have been widely utilized in petro- chemical industries as well as sustainable chemistry areas. Synthesis of zeolites often requires small molecules termed Organic Structure Directing Agents (OS- DAs), which are critical in forming the porous structure. Molecule generation models aid the design of OSDAAs, but they are limited by single functionality and lack of interactivity. Meanwhile, large language models (L..Ms) such as GPT- 4, as general- pereral-purpose artificial intelligence systems, excel in instruction compre- hension, logical reasoning, and interactive communication. However, LLMs lack in-depth chemistry knowledge and first frstt-principl computation capabilities, result- ing in uncontrollable outcomes even after fine-tuning. In this paper, we propose OSDA Agent, an interactive OSDA design framework that leverages LLMs as the brain, coupled with computational chemistry tools. The OSDA Agent consists three main components: the Actor, responsible for generating potential OSDA structures; the Evaluator, which assesses and scores the generated OSDAs using computational chemistry tools; and the Self-reflector, which produces reflective summaries based the Evaluator's feedback refine the Actor's subsequent outputs. Experiments on representative zeolite frameworks show the generation- aealuation-efieettoo--eeeeeeeeee
workflow can perform de novo design of OSDAs with superior generation quality than the pure LLM model, generating candidates consistent with experimentally validated OSDAs and optimizing known OSDAs.
INTRODUCTION
Zeolites, class of microporous silicate-based materials, have been widely used as highly efficient catalysts and adsorbents, in petrochemical industries and sustainable chemistry processes (Davis) 2002). The unique properties of zeolites stem from their porous frameworks and the synthesis of zcolites often requires the use small molecules named Organic Structure Directing Agents (OS- DAs) Moliner et al. 2013). OSDAs act templates influencing the size, shape, and connectivity of the pores of zeolites. The design of effective OSDAs crucial for tailoring the properties of the target zeolite, but this process has traditionally been guided by empirical knowledge and labor- intensive trial-and-error methods.
Recently, artificial intelligence has been extensively applied across various scientific disciplines, including chemistry (Liu et al., [2023] Jiang et al.] (002)), biology (Fan et al. 2022 Corso et al.) Fan al. (2023), and materials (Sriram et al. [2224] Gruver et al.) 2024] Yang et al. 2024 Zeni et al.) 2023). In particular, artificial intelligence has significantly influenced the design of OS DAs. Various heuristic algorithms, such as genetic algorithms (Pophale et al.) (003)) and colony algo-
Yi Yang and Xin Li are the corresponding authors.

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rithms (Muraoka al. 2020), have been employed to explore the vast chemical space of potential OSDAs, identifying promising candidates. Machine learning has also made sigmificant strides in
this area (Chong et al. 2020 Xie et al., 2019)] Manuel Serra et al. 2007 Moliner et al.) 2019). With
the development of large-scale zeolite synthesis datasets (Pan et al. 2024] Muraoka et al. 2019]
Jensen et al. 2021), neural networks trained on these extensive datasets can predict the suitability of
OSDAs for specific zeolite structures. These networks effectively learn from established synthesis
recipes and experimental results, enabling the design of OSDA molecules (Jensen et al. 202] Xu
et al. 2023). Although these methods enable OSDA predictions, their inability to accept feedback from human experts limits the interactability of the model.
Large language models (LLMs), such as GPT-4 developed by OpenAl (OpenAl| (023). are capable of processing and generating human-like text and performing complex reasoning based on exten- sive datasets. These models have demonstrated remarkable proficiency in various natural language
processing and multimodal tasks, including text generation, translation, summarization, and multi-
modal understanding (Raffel et al. 2020; Brown 2020, Devlin 2018 Li et al. Zhang et al. 2024|] Li et al. 2025). OSDA molecular design based on LLM can provide a more natural and seamless user experience that goes beyond traditional machine learning methods, with more aspects of the model accepting suggestions from chemical experts, leading to better generation (Ito et al. (2024b|a), a). While LLMs can process vast amounts of data and generate novel molecular structures, they often lack the domain-specifie reasoning and ability to effectively integrate complex experimental constraints or optimization criteria (Burtsev al. 2023).
In this paper, we introduce the OSDA Agent, an innovative interactive framework for the design of OSDAs that harnesses the capabilities of large language models as the core intelligence, comple- mented by advanced computational chemistry tools. The OSDA Agent comprises three key com- ponents: the Actor, tasked with generating a diverse array of potential OSDA structures based on predefined criteria, utilizing the generative capabilities of LLMs to explore vast chemical space and synthesize novel molecular architectures; the Evaluator, which critically assesses and scores these OSDAs using suite of computational chemistry tools (Landrum [013 Coley et al. 2018), 1), employ- ing various metries to determine the feasibility and effectiveness of each proposed structure; and the Self-reflector, which plays pivotal role in the iterative design process by producing comprehensive reflective summaries (Shinn et al.] 2024) based on the Evaluator's 's feedback, guiding the Actor in refining its outputs to enhance the quality of subsequent designs. Through the integration of these components, the OSDA Agent not only streamlines the design process but also fosters a continuous learning environment, enabling the system to adapt and improve over time, representing signifi- cant advancement in the automated design of OSDAs and facilitating more efficient and effective approach zeolite synthesis. In summary, our contributions are threefold:
We introduce the OSDA Agent, an innovative interactive framework that integrates LLMs with computational chemistry tools for the de novo design of OSDAs given target zeolites.
To cope with the problem of uncontrollable results generated by LLMs, we introduced re- flection mechanism that consists of a novel binding-energy prediction module and chemical traditional tools.
Our experimental results demonstrate that the OSDA Agent significantly improves the qual- ity of OSDA candidates compared to traditional methods, yielding structures that align with experimentally validated OSDAs and effectively optimizing known OSDAs.
2 RELATED WORK
Designing OSDAs for Zeolites. The interaction between OSDAs and zeolites can be captured through atomistic simulations, such as density functional theory (DFT) and molecular dynamics simulations, though these methods require substantial computational resources (Schwalbe- & 2021a). Jensen et al. (Jensen et al. (021) utilized data-driven approaches to explore relationships between OSDAs, qualitative gel chemistry, and zeolite structures. Dacyaert et al. (Daeyaert et al. 2019) and Xu et al. (Xu et al. 2023) applied machine learning to train neural networks for designing OSDAs tailored to specific zeolite structures. Recent studies by Ito et al. (Ito et al., 2024b a) investigated the use of LLMs in designing novel OSDA molecules, highlighting their potential for advancing molecular innovation. In contrast to previous approaches, our method combines the knowledge embedded in LLMs with chemical tools to improve design outcomes.
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LLM Agents for Science, Large language model (LLM) agents now widely used across vari-
ous scientific disciplines (Chiang et al. 2024; Huang et al. 2024, Skarlinski et al. 2024: Ma et al.
2024 Ghatarollahi & Buchler, [024]] Lála al.] [023] Boiko al. 2023 Darvish et al. 2024). ChemCrow (M. Bran et al., 2024) developed an L.M agent integrated with specialized tools for
literature retrieval, molecular modification, and reaction execution, enabling the autonomous execu- tion of chemical synthesis. Several studies (Buchler) (2024) Sprueill et al. [2024] Ansari & Moosavi, 2023 Kang & Kim 2024) have explored the application of LLM agents in the field of materials science. Furthermore, MedAgents (Tang et al., 2023) and DRUGAGENT (Inoue et al., (024) have explored the applications of LLM Agents the fields of medicine and drug discovery. this work, we investigate the application of an LLM Agent for OSDA molecule design.
Text-based de novo Molecule Generation. In recent decades, Al-driven methods have become central to advancing molecule discovery (Hu et al. 2023 Fan & Yang 2024). Text-based de novo molecule generation utilizes natural language processing techniques alongside chemical information to design novel molecular structures. Text2Mol (Edwards al., (021), constructs paired datasets of molecules and textual descriptions to learn a shared semantic embedding space for improved re- trieval. MolT5 (Edwards al. 2022) and BioT5 (Pei et al., (023), built on the T5 (Raffel al.) 2020) architecture, are pre-trained on large corpora of text and molecular strings, enhancing their ability to generate molecules from textual input. MoMu (Su al. (022) uses contrastive learning to bridge molecular graphs and related text data. TGM-DLM (Gong et al. 2024) explores diffusion models for molecular generation. Moreover, FrontierX (Sakhinana & Runkana 2023) and Mol- ReGPT eLi al., 2024) utilize the capabilities of large language models, presenting new paradigms for molecular generation. However, traditional mo-oeulll methods struggle to perform well when faced with the complexity of designing OSDA molecules. In this work, we address this chal- lenge by utilizing the comprehensive capabilities of the LLM Agent.
3 PRELIMINARIES AND DATA DESCRIPTION
3.1 DATA
We utilized the Zeolite Organic Structure Directing Agent Database (OSDB) (Schwalbe-Koda al., 2021] that includes 112,400 OSDA-Zeolit pairs which comprises 549 OSDA molecules and 209 distinct zeolite frameworks extracted from the literature. The database provides the corresponding binding energy for each OSDA-Zeolite pair along with the OSDA-Zeolite complex structure data saved in CIF files. In addition, for each OSDA molecule, we calculated its synthetic complexity score (SCScore) (Coley et 2018).
Furthermore, we employed the Jensen dataset (Jensen et al. 2021) extracted from the entire body of zeolite literature, spanning over 140 journals from more than 15 publishers between 1966 and 2020. includes 5,663 synthesis routes extracted from 1,384 articles, encompassing OSDA molecules, synthesis gel compositions, and the resulting zeolite phases. The dataset provides details on 758 distinct OSDA molecules and 205 zcolite phases, from which we extracted OSDA molccules as real-world examples of existing OSDAs.
3.2
THE STRUCTURE OF ZEOLITE COMPLEX
The OSDA complex is a variation of crystal structure that still adheres to the geometric constraints of crystals. Thus, we represent it as a crystal graph. In the unit cell U = P), X = [x,, [2,, ... -I,-1, xn,]² E [R=x1, where n represents the number of atoms, and x; E g represents the type of atom in the unit cell. P = (P.,P2..... Pa-11. P.,'T E R=*3 is the atomic position matrix, where p: R3 represents the Cartesian coordinates of atom i in the unit cell in three-dimensional space. To further encode the periodic pattern, additional lattice vectors L = (2) R3x3 are used to describe how the unit cell repeats in three directions. Thus, in 3D space, we represent the Zeolize complex as (X, P. L). see Figure 11 in Appendix E
3.3 BINDING ENERGY
The affinity of an Zeolite pair can be guantitatively reflected via the binding energy. A smaller binding energy value indicates that the OSDA is more compatible with the zeolite. The
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calculation formula of binding energy is:
Es Ezeo-onda Ezeo
Eusda
where Eg represents the binding energy, Erco-osda is the energy of the OSDA-Zeolite complex, Ezeo the energy of the zeolite, and Bodda the energy of the OSDA (Schwalbe-Kow & Gómez- Bombarelli 2021a).
METHODOLOGIES
4.1
OSDA AGENT FRAMEWORK
In developing the OSDA Agent framework, we followed the methodology described in (Shinn et al., 2024]. Our framework integrates three interconnected models: the Actor, the Evaluator, and the Sell- reflection model. The Actor is responsible for creating OSDAs by employing advanced algorithms that guide molecules into desired configurations. The Evaluator assesses the OSDA molecules gen- erated by the Actor, utilizing various chemical tools for evaluation. The Self-reflection model lever- ages feedback from the Evaluator to refine the synthesis processes within the Actor, continuously improving the efficacy of the organic structure-directee agents. This cohesive approach provides a robust framework for generating and optimizing OSDAs. The overall framework architecture is illustrated in Figure (a).
More specifically, the Actor in this work assumes the role of a zeolite expert. The Actor is based on LLM that generates SMILES sequences for OSDA molecules in response specified prompts and requirements. In this context, we utilize In-Context Learning (Yoo et al. (02T]) and Chain of Thought (Wei al. (002)) techniques guide the Actor's performance. The specific prompt strategies we employ are detailed Section[4.2 Additionally, we incorporate a memory component, which provides supplementary context to enhance the Actor's capabilities.
The Evaluator component of the OSDA Agent framework plays a critical role in assessing the quality of the OSDA molecules generated by the Actor. It takes the candidate SMILES sequences produced by the Actor input and employs range of chemical tools for evaluation. The chemical tools we use consist of a series of small models and predefined rules. First, RDKit is utilized verify that the generated SMILES expressions adhere to chemical validity. Then, the Evaluator provides comprehensive assessment by scoring the potential OSDA molecules based on their binding energy and SCScore (Coley et al., 2018)). This approach ensures that the generated OSDA molecules meet the required specifications and are synthetically feasible, as detailed in Section 4.3
Although LLMs exhibit powerful intelligence, they occasionally make reasoning errors (Madaan jet al., 2024). To mitigate such errors, we implemented a self-reflection module. Based on a LLM, this module provides valuable feedback for future iterations through verbal self-reflection. It gen- crates detailed and specific feedback based on the assessments from the Evaluator, the current gen- erated results, and the memory of the agent. In subsequent trials, the agent leverages its past periences to optimize the generation of OSDA molecules. This iterative process of trial and error, self-reflection, and persistent memory enables the agent rapidly enhance its pocisiin-maaingg abil- ities across various environments by effectively utilizing feedback signals.
4.2 STRUCTURE DESIGN WITH COT STRATAGY
To address the limitations of LLMs in handling complex reasoning tasks, we adopted few-shot Chain of Thouggh (few-shot CoT) approach. By designing specialized prompt strategies, we aim to foster more continuous and incremental reasoning process within the model, enabling to more effectively generate valid OSDA molecules.
The few-shot CoT prompt combines In-Context Learning (ICL) (Brown 2020) with the Chain of Thought (CoT) (Wei al. 2022) technique. By introducing continuous, step-by-step reasoning process through the prompt "Let's 's think step by step." we effectively guide the model solving complex tasks. CoT enables the model to break down tasks into their constituent parts, leading to clearer and more logical solutions (Wei et al.] (022). As a few-shot prompt, we also provide the L.L with an example help it better understand and respond to the required format and content.
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(a)
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Figure 1: OSDA Agent Framework. (a) The OSDA Agent pipeline, integrating three models: Actor, Evaluator, and Self-reflector, responsible for generating molecules, performing chemical evalua- tions, and providing optimization feedback. (b) The Evaluator uses three chemical tools to assess molecules based chemical principles, synthetic feasibility, and binding energy. (c) Example of OSDA Agent in action.
In crafting the prompts for the generative large language model, we also employed role-play in- structions, assigning the model the role of an expert in zeolite OSDAs. Additionally, we extracted several well-performing OSDA molecules from the OSDB database to serve as context. The com- plete prompt can be found in Figure 7 and Figure in Appendix D
4.3 EVALUATION WITH CHEMICAL TOOLS
The LLM Agent leverages its powerful natural language processing capabilities to intelligently gen- erate molecular structures. However, due to the intricate chemical constraints and reasoning require- ments, the output generated by the LLM alone may not fully meet the precision required in chem- istry. Therefore, we introduced a series of chemical tools to ensure that the generated molecules adhere chemical rules and possess synthetic feasibility. This combination provides with robust and flexible framework for exploring and optimizing new OSDA molecules.
We incorporated three key chemical tools to help the agent improve the chemical validity and ac- curacy of the generated OSDA molecules. These tools include the RDKit (Landrum) (013)) toolkit, SCScore (Coley al. 2018). and the binding energy estimation model. In the following sections, we will provide a detailed introduction to each of these tools.
We also developed memory component that stores the evaluation results from the chemical tools for the generated molecules. These stored evaluations are utilized by the Reflection Mechanism to inform and improve subsequent iterations.
4.3.1 RDKIT TOOLKIT
The LLMSs occasionally generate OSDA molecules that do not meet the required standards, including violations of chemical rules and OSDA-spccific empirical guidelines. To address this issue, we used the RDKit (Landrum) 2013) toolkit to evaluate and filter the generated molecules. The specific empirical criteria, similar to those outlined in (Ito et al. 2024b), are listed in Table Appendix
4.3.2 SYNTHETIC COMPLEXITY SCORE
The synthetic accessibility of OSDAs is a crucial factor in assessing their quality. In our work, we employed SCScore assess synthetic complexity. SCScore is scoring method based reaction database learning, where a neural network model is trained on a large dataset of 12 million reactions from the Reaxys database predict the number of reaction steps required synthesize the target
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(c) DSDA SMELSS
(b) Input
SMILES Encoder
C1GCCNCOC1
SMILES BERT
CONDRIORIN
Feed Forward
3D Malentee
Zedite Sncarauds
SMILES string
Self Altention
Zeolite Graph Encoder
VOID
CGCNN
Aallle energy
Zeolite framework
Complex poses
Complex Graph Encoder
CGCNN
Output
Network
-8 9.9
complex
Figure 2: Details of the binding energy estimation model. (a) Process: We provide (OSDA, Zeolite) pairs to the VOID tool generate docking poses. The predicted energy for each pose then com- puted using neural network model, and the lowest is selected as the estimated binding energy. (b) Neural network architecture: The input consists three types of information relevant deter- mining the binding energy: OSDA, Zeolite, and Complex. We use different architectures to extract information from each and then integrate them to predict the binding energy.
molecule (Coley et al. 2018 Lawson et al. 2014). The model establishes a nonlinear metric by comparing the synthetic complexities of reactants and products, effectively capturing the increasing complexity across multi-step synthesis routes. The SCScore ranges from I to 5, with lower values indicating that molecule is easier to synthesize.
4.3.3 BINDING ENERGY ESTIMATION MODEL
Since each OSDA Agent design involves evaluating the quality of the OSDA design multiple times using binding energy, traditional atomic simulation methods for calculating binding en- engy often require computing the energy of dozens to hundreds of complexes, which is time- consuming (Schwalbe-Kood Koda Gómez-Bombarelli 202 To address this issuc, we proposed a deep learning-based method estimate the energy of these complexes, significantly reducing the time required for binding energy calculations, as shown in Figure (b).
Our inspiration stems from the calculation formula for the OSDA-Zeolin binding energy. In Equa- tion equation 1 the binding energy related to the energy of complex, the energy of zeolite, and the energy of OSDA. We follow this principle in approach. The inputs molecular SMILES, zcolite structure diagrams, and complex structure diagrams. The zcolite struc- ture diagrams are obtained by parsing CIF files. We obtain the complex's CIF files using the VOID (Voronoi Organic-Inorgane Docker package) (Schwalbe- Koda & -oobae-Bombarlliii 2021b) and parse them extract the complex structure diagrams.
method include
We utilize pre-trained CHILES-BERY (Wang et al. 2019) model to extract molecular features, while the complex and zeolite structure diagrams are processed using CGCNN (Xie Grossman 2018) networks to extract complex and structural features. Then, these features (molecular, struc- tural, and complex) are concatenated, followed by feature fusion using transformer, and finally, fully connected neural network is used predict the complex energy. Our model is trained data from the OSDB database. The effectiveness of each module is shown Table 3 in Appendix
Throughout the entire process, candidate OSDA molecular SMILES are generated using Actor, and the 3D conformations of the molecules are optimized using the MMFF94 (Halgren 1996) method.
Possible binding poses between the 3D molecular conformations and zeolite structures generated using the VOID tool. We predict the energy for each pose and use the lowest energy as the estimated binding energy, illustrated in Figure (a).

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4.4 REFLECTION MECHANISMS
The Reflection Mechanism reads the evaluations of the OSDA molecules generated by the Actor, stored the memory component. Using a specially designed reflection prompt, the Self-reflection LLM provides constructive feedback on the Actor's output. This process repeats for set number of iterations, guiding the Actor to improve the quality and success rate of the generated molecules.
The reflection prompt utilizes a few-shot prompting technique, providing the Self-reflection LLM with several examples of constructive feedback. We instruct the Self-reflection LLM to critique and offer suggestions the generated results based on the evaluations from the chemical tools. The feedback can focus on several aspects, including the chemical validity of the generated molecules, their synthetic feasibility, and the estimated binding energy with the given zeolite structure. The goal is to produce OSDA molecules that are both effective and easy to synthesize. The complete prompt and examples can be found in Figure and Figure T0 in Appendix D
EXPERIMENTS
5.1 EXPERIMENT SETUP
In our OSDA Agent framework, the Actor utilizes the GPT-4 model, while the Self-reflection is based the GPT-4o model. The examples used in the few-shot CoT prompts are sourced from the OSDB database. We built our framework using Langchain, and the RDKit version employed 2023.9.1. In the experiments, the literature OSDAs were obtained from the Jensen dataset (Jensen cct al. 2021). Baseline methods include state-of-the-an text-based de novo molecule generation approaches, such BioT5 (Pei et al. 2023), MolT5 (Edwards et al. 2022).
The binding energy estimation model was trained on OSDB data. The model was trained for 15 epochs using the Adam optimizer, with le-5 learning rate and batch size of 32. It was implemented in Python v0.10.4 with the PyTorch v1 v1.12 framework and trained on an NVIDIA A6000 GPU. To ensure the independence of the training and test sets, the dataset was split based on SMILES, follow- ing an 8:1:1 ratio. This split ensured that the OSDA molecules in the training set were completely distinct from those the test set. The details and results can be found in Appendix E
5.2 RESULTS OF OSDA MOLECULE DESIGN
To validate the effectiveness of our approach, we selected two representative zeolite frameworks for testing: the small-pore LTA framework (Reed & Breck 1956) with cage-like structure and the large-pore one-dimensional AFI framework (Bennett et al. 1983). We applied our method to design OSDA molecules tailored for these two zeolite structures. In this section, we focus presenting the results and analysis of our method in designing OSDAs for the cage-like small-pore LTA frame- work and the large-pore one-dimensional AFI framework. Additionally, we extended our method to test other zeolite frameworks, including LEV (Barrer & Kerr 1959), AEI (Simmen et al. 1991).
AFX (MeGuire al. 1995), ITE (Camblor et al. 1997) and MOR (Simoncic & Armbruster with the results provided in Appendix
2004
5.2. SUGGESTED OSDA CANDIDATES
Figure 3 presents several OSDAs predicted by OSDA Agent using de novo molecular de- sign workflow. Compared to previous work (Ito et al. 2024b a), our method incorporates synthetic feasibility into the model, resulting in OSDA candidates that only match known OSDAs but also exhibit a high degree of synthesizability. Additionally, consultations with experts in zeolite chemistry affirmed that the generated molecules exhibit significant potential function as OSDAs.
5.2.2 DIMENSIONALITY REDUCTION ANALYSIS
An essential capability for any molecular generation model is to capture the molecular distribution and generate diverse and realistic molecules. Such capabilities paramount when constructing virtual libraries to advance computer-aided drug discovery endeavors (van Hilten et al.) 2019). To evaluate the performance of model in generating OSDAs, performed comparative analysis
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Sescorem 1.51
Sessorem
PPO
Sescorem 1.718
Sescorem
7.769
-6653
7.960
6.336
Sescorem
Scscore=1.332
Sescoce=1.7 1.736
Sescore- 1.852
Eg
6.787
8g = 6,939
Sescore= Fg 0.600
Scscorew 1.901
Sesowre=1448 1.42
Sescore=2.072
Eg 7.569
Bg 6,737
Bg = 7.252
Soscore=1.366
Sesovre=1.366
Sescorem 1.333
Scscore=1.500
8g 7.117
6.613
7.508
6,600
Figure 3: Examples of OSDAs for two representative zealite frameworks, large-pore AFI framework and small-pore LTA framework, generated using the OSDA Agent. The Fs is measured in kJ/mol Si.
against previously reported OSDAs using Principal Component Analysis (PCA) based on Weighted Holistic Invariant Molecular (WHIMM (Todeschini Gramatica 1997) descriptors (detailed in Ap- pendix C
PCA Analysis: OSDAs for AFI
Literature Generated
Literature
10
Generated
10.0
7.2
PCA
PCA
-25
PCA
Figure 4: Principal Component Analysis (PCA) of WHIM vector representation for OSDA molecules used in AFI framework. PCA 1, 2, and 3 represent the first three principal axes. Red points show OSDAs generated by the OSDA Agent, blue points represent OSDAs of AFI frame- work from the literature, and gray points represent all OSDAs extracted from the literature.
The dataset used for this analysis includes all OSDAs from the literature (shown in gray), OSDAs generated by our model (shown in red), and OSDAs of specific frameworks of zeolite previously reported in the Jensen dataset (shown in blue). WHIM descriptors, which capture molecular ge- ometry based on principal components derived from molecular field methods, were calculated for both datasets. These deseriptors were then standardized and processed via PCA to visualize and assess the overlap between the OSDAs generated by our model and those previously reported in the reducedddimmensionaltyy space.
Consistent with previous work[Jensen et al., 2021 Xu et al. 2023). we used PCA to reduce the WHIM descriptors to three dimensions. The explained variance the principal components con- firms that PCA 1, PCA 2, and PCA 3 capture the majority of the variance in the data (Figure in Appendix C We conducted tests on AFI and LTA frameworks, with the PCA results shown in Figures 4 and Figure 14 in Appendix Fl displaying data points projected onto the first three prin- cipal components. The left plot (PCA vs. PCA 2) illustrates the distribution of all OSDAs from the Jensen dataset (in gray), OSDAs generated by our model (in red), and OSDAs of AFI and LTA
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frameworks previously reported in the literature (in blue). The right plot (PCA 1 vs. PCA 3) offers a complementary view of the data distribution.
This visualization demonstrates notable overlap between the generated OSDAs and the reported OSDAs, which indicates that our model has successfully generated molecules that closely align with known, experimentally validated OSDAs. Furthermore, the red points suggest that our model is also capable of generating novel OSDAs that explore new regions of the molecular descriptor space, highlighting the generative model's potential for de novo OSDA design.
5.2.3 PERFORMANCE COMPARISON
To evaluate the effectiveness of our OSDA Agent framework, we compared the results of our ap- proach with two text-based de novo molecule generation methods, MolT5 and BioT5, as well as the results generated by the pure GPT-4 model without the additional enhancements provided by our integrated method. To ensure a fair comparison, we supplied the GPT-4 model with the same OSDB data used for prompts in the OSDA Agent. We generated approximately 100 OSDA molecules each for both AFI and LTA frameworks using the OSDA Agent and GPT-4, while MolT5 and BioT5 produced around 500 OSDA molecules each for both AFI and LTA frameworks.
In this study, we employed seven well-established metrics (detailed in Appendix C to assess the capability of our approach to generate OSDA molecules that align with real- distributions and empirical rules. The first metric is Validity, which evaluates whether the generated molecules comply with the empirical rules for OSDAs, as shown in Table of Appendix [ Next, we utilized four commonly used eet-to-molecule metrics BLEU (Papineni et al.] (002), Morgan, MACCS, and RDK measure the similarity between the generated OSDA molecules and those reported in the literature. Finally, we applied the Energy Distance (ED) (Székely & Rizzo 2013) and Kullback- Leibler (KL) divergence (Kullback & Leibler 1951) to assess the distributional similarity between the generated OSDAs and the ones reported in the literature.
As shown in Table T. traditional text-to-molee molecule methods like MolT5 (Edwards et al. 2022) and BioT5 (Pei et al. 2023) struggle to generate molecules that comply with OSDA rules, largely due to their lack of specialized knowledge in zeolite OSDAs. The molecules they generate significantly differ from those reported in the literature. While large language models such GPT-4 possess the relevant knowledge to produce more plausible OSDA molecules, they lack the guidance of special- ized chemical tools. As a result, GPT-4 generated OSDA molecules exhibit significant shortcomings in terms of accuracy and diversity. Specifically, more than 50% of the OSDAs generated by the pure GPT-4 model do not meet OSDA requirements. Although certain similarity metries (e.g.. Morgan) show relatively high scores, the generated molecules exhibit significant differences in their overall distribution compared to the OSDAs reported in the literature. This issue is clearly illustrated in Figures 15 in Appendix [F] where the distributions of molecular volume, molecular weight, and as- phericity are visualized. Notably, the molecular volume and weight of the purely GPT-4 generated molecules deviate substantially from those of the OSDAs found in the literature.
Our OSDA Agent integrates the GPT-4 model with targeted prompt strategies, chemical validation tools, and a reflection mechanism, consistently producing OSDAs with higher chemical relevance and synthesizability. The inclusion of the SCScore model and RDKit for chemical rule validation allows our framework to filter out unfeasible molecules early in the process. Additionally, the flection mechanism iteratively refines the generated OSDAs, enhancing their aligmment with known experimental data and synthesis routes. This capability enables the OSDA Agent accurately gen- erate molecules that conform to OSDA rules and exhibit greater similarity to those found in the literature. As shown in Table [ our OSDA Agent outperforms other methods across the majority of similarity metrics. In terms of distribution, the OSDA molecules generated by the OSDA Agent exhibit lower KL divergence and Energy distance (ED) values. Therefore, we conclude that the OS- DAs generated by the OSDA Agent are more representative of those found in real-world scenarios.
5.3 RESULTS OF OPTIMIZING EXISTING OSDA MOLECULES
In the previous sections, we have demonstrated the capability of our OSDA Agent in molecular design, and we hope that our approach can also be effective in optimizing existing OSDA molecules. To demonstrate the capability of our approach in refining complex and challenging synthesized molecules, applied the model to several OSDAs previously reported in the literature. These

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Table 1:
The performance of the OSDA Agent in the de novo design of OSDAs,Th best scores are
in bold, and the second-best scores are underlined.
Type
Method
Validity?
BLEU
Morgant
MACCS1
RDK
EDJ
KL Divergence
MolTS
(Edwards
2022
0.000
0.204
0.157
0.556
0.384
266.4
7.753
BioT3
(Pei et al. 2023
0.008
0.193
0.147
0.384
0.162
120.7
3.128
GPT-4
0.440
0.554
0.332
0.750
0.591
4.061
0.653
OSDA Agent
1.000
0.577
0.308
0.781
0.608
2.567
0,588
MolTS (Edwards
2022
0.000
0.266
0.151
0.520
0.297
189.4
2.985
BioT5
(Pei ct al.
2023
0.005
0.228
0.130
0.431
0.337
73.43
1,093
LTA
GPT4
0.471
0.382
0.213
0.677
0.360
12.80
0.976
OSDA Agent
1.000
0.499
0.229
0.737
0.412
2.084
0.744
molecules, selected from the OSDB database, were chosen specifically for their complexity and synthesis difficulty. The goal was to enhance synthetic feasibility while maintaining low binding energies.
We tested the ability of our OSDA Agent to opti- mize existing OSDA molecules for AFI framework. We selected ten OSDA molecules from the OSDB database with relatively high SCScore, averaging around 3.45. Using our approach, we modified the prompt to "Can you start with this molecule and op- timize this OSDA molecule..."" and set the reflec- tion steps to 4 for each molecule. To ensure the re- liability of the results, we repeated the optimization process five times for each of the molecules.
Suscore
4.
-X-
Brnd ng Energy
Binding Energy [Rtmmol Si1
35-
SCScore
620
7701
+52
2,2
We calculated the average SCScore and binding en- engy across the five experiments, as shown in Fig- 5 The results indicate that the average SCScore decreased from 3.45 to 2.46, suggesting a lower synthesis difficulty. The optimized binding energy averaged -6.71 kcal/mol, which remains within the reasonable range (-3.38 kcal/mol -9.00 kcal/mol) based on the Jensen dataset. Additionally, the optimized molecules maintained structural similar- ity to the originals, preserving the functional properties necessary for zeolite templating. Detailed optimization results can be found in Appendix Figure [7
2.0
Number of reflections
Figure 5: During the molecular optimization process, the SCScore and Binding Energy of the OSDA molecules varied with the number of reflection iterations.
The results indicate that our OSDA Agent successfully generated optimized versions of these molecules, reducing the SCScore and thereby lowering the synthesis difficulty, while preserving the functional characteristics necessary for zeolite templating. This demonstrates that the OSDA Agent has the potential not only to design new molecules but also to improve existing ones.
CONCLUSION
In this work, we introduced the OSDA Agent, which effectively designs and optimizes OSDAs through iterative refinement with targeted prompts. Applied to small and large pore frameworks, including LTA and AFI, the OSDA Agent generated chemically valid and structurally compatible molecules. Comparisons with literature-reported OSDAs revealed competitive, and some cases superior, designs. These results highlight the OSDA Agent's potential for molecular design and op- timization. Future work will focus on expanding the applicability of our methods to a broader range of scientific fields and further integrating experimental constraints into the optimization process.
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ACKNOWLEDGMENTS
This work are supported by National Science and Technology Major Project (000),,)020801), National Natural Science Foundation of China (62472381) and Fundamental Research Funds for the Zhejiang Provincial Universities (226-2024-002288)
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A LINK TO DATASETS AND MODELS
Jensen dataset: http:////tttt.ooooo/oaaat/r//r/r
OSDB database: http:s//eeob..ntteeed//neee
Pre-trained htt.ha/i....aaaaaaa///ee/aaaaaaa
GPT-4: httpsr//pee.aal.cc///httttttttt VOID: httptt////atttt..cooo//ssaaaaatt
Matni//owouaaunoogggoooee/////gg Mistral--Denn--nsstro0----11--1- 1-70B-Instruct
B SUPPLEMENTING THE BACKGROUND AND MOTIVATION
The Importance of OSDA in Zeolite Synthesis
In zeolite synthesis. Organic Structure-Directn Agents (OSDAs) are essential in determining the final framework structure and morphology of the material. Zeolites are microporous materials with wide range of applications catalysis, adsorption, and ion exchange. The specific properties of ze- olites, such as pore size and shape, depend on their crystalline structure, which is directly influenced by the presence of OSDAs. These organic molecules act as templates during the synthesis process, guiding the formation of the zeolite framework and ensuring the desired structural arrangement.
OSDAs not only direct the crystallization of zeolite but also contribute to the stability of the result- ing material. In some cases, OSDAs function integrity of the aluminosilicate framework during synthesis. Without the appropriate OSDA, zeolite formation may either fail to occur result in amorphous materials with poorly defined structures. Therefore, the choice of OSDA critical in controlling the final zeolite structure, affecting both the framework topology and the material's performance.
balanggg agents, helping to maintain the
Why do the design of OSDAs currently present significant challenges?
OSDA plays crucial role in determining the topology of zeolites, especially in the formation of their porous structures. Traditional methods mainly rely on two approaches: first, using empirical experience and experimentation search for feasible OSDA molecules within molecular library; and second, employing computational simulations of zeolite-OSDA interactions (such as Density Functional Theory, DFT) assist in OSDA design. Both experience-based and simulation-based traditional methods are highly time-consuming and resource-intensive. In particular, the vast chem- ical space of OSDA (small molecules) presents significant challenges in discovering new OSDA candidates using conventional methods.
Moreover, due the difficulty in finding new OSDA molecules, the existing pool of OSDA candi- dates is very limited. For example, the Jensen dataset, which collects data from papers published between 1966 and 2020, contains only 758 different OSDA molecules. Traditional machine learn- ing generation methods typically require large datasets, but the small number of OSDA molecules makes challenging to train effective generative models. Several traditional generative model ap- proaches(Jensen et al. 2021 Xu et al. 2023) are typically trained using synthetic routes. This means that when designing OSDAs, information related to the synthetic pathway, such as gel chemistry, must be provided. Moreover, the limited number of different OSDA molecules in the dataset nega- tively impacts the model's 's ability to search effectively within the chemical space. Currently, Large Language Models (LL.Ms) have broad knowledge across related fields, and we aim leverage the domain expertise of L.LMS to overcome the data scarcity issue.
Why is an iterative, feedback-based approach employed in this context?
Due to the phenomenon of "hallucination" in large language models (LLMs), especially when per- forming complex tasks like molecular design, is necessary to introduce additional chemical knowl- edge to help the LLMs accomplish the task more effectively. Through an interactive, feedback- driven approach, we professionally evaluate the molecules designed by the LLM,, including factors
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such OSDA empirical rules, molecular synthesis difficulty, safety, etc., and provide feedback to the LLM. This process is similar to how chemists validate through experiments and make improve- ments based on the experimental results.
C EXPERIMENTAL DETAILS AND METRICS
We outline the metrics employed evaluate the performance of the generative models in our exper- iments, encompassing:
Validity. The proportion of generated molecules that follow empirical rules OSDAs can be expressed as:
Nvalid
Nuatl
where Nvaid is the number of valid molecules and Niatl is the total number of generated molecules (see Table 2).
BLEU. To assess the difference between the generated molecules and OSDAs reported in the liter- ature, the evaluation is conducted by calculating the average BLEU score B between the SMILES representations of the generated molecules and those of the closest OSDAs documented in the liter- ature:
N N BLEU(S SOSDA,
B
where N is the number of generated molecules, Sgen,s is the SMILES representation of the i-th generated molecule, and SOSDA, = SOSDA. with
= arg max BLEU(SSeente SOSDA.)),
where j iterates over all OSDAs, ensuring SOSDA, the SMILES representation of the OSDA most similar the i-th generated molecule.
Morgan. Morgan fingerprints, which generate feature vectors by calculating the local atomic envi- ronments in molecules, used here measure the difference between the generated molecules and the OSDAs reported in the literature. Specifically, this is done by calculating the similarity SM between each generated molecule and its closest OSDA documented in the literature, followed by averaging these scores:
N sim Fgen, FOSDA,
S Mooggnn
where N is the number of generated molecules, Fg is the Morgan fingerprint of the i-th generated molecule, and FOSDA, FOSDA,j* with
= arg max sim siimFgen..ss (egm,ss FOSDA.)),
where j iterates over all OSDAs, ensuring FOSDA. is the Morgan fingerprint of the OSDA most similar the i-th generated molecule.
MACCS. MACCS fingerprints generate a fixed-length bit vector based on a predefined set of struc- tural subgraphs chemical features, with each bit representing the presence or absence of a specific chemical feature. Here, we use MACCS fingerprints to evaluate the difference between the gener- ated molecules and the OSDAs reported in the literature. Specifically, this is done by calculating the
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average similarity SMACES between the MACCS fingerprints of the generated molecules and those of the closest OSDAs documented in the literature:
1 SACCs = N
Σ sim (Fgee, FOSDA,"*)
where N is the number of generated molecules, ggn, is the MACCS fingerprint of the i-th generated molecule, and FOSDA."- JODDA.j" with
arg max sim FOSDA.;),
with j iterating over all OSDAs, ensuring FOSDA, is the MACCS fingerprint of the OSDA most similar to the i-th generated molecule.
RDK. RDK fingerprints generate feature bit vectors by traversing the bond paths in a molecule. Here, we use RDK fingerprints to evaluate the difference between the generated molecules and the OSDAs reported
the literature. Specifically, this done by calculating the average similarity SRDK between the RDK fingerprints of the generated molecules and those of the closest OSDAs documented in the literature:
1 Σ sim Com(Faen Fg FOSDA, ):
SROK
where N is the number of generated molecules, Egen, is the RDK fingerprint of the i-th generated molecule, and FOSDA, FOSDA.J" with
arg max sim FOSDA.)),
J
with iterating over all OSDAs, ensuring FOSDA, is the RDK fingerprint of the OSDA most similar to the i-th generated molecule.
THIM.WHIM descriptors are class of molecular descriptors used to characterize the three- dimensional structure of molecules by considering global properties such molecular geometry, mass distribution, polarity, and electron density. Here, we use WHIM descriptors to calculate the dif- ference between the distribution of the generated molecules and the distribution of OSDAs reported in the literature. WHIM descriptors capture information about a molecule's 's three-dimensional con- formation, including its size, shape, symmetry, and atomic distribution. Due molecular flexibility, different conformations can result in significantly different WHIM representations. For example, long linear molecule can either stretch out or fold, leading two distinct 3D representations. To address this challenge, we calculated the average conformation WHIM descriptors using the geome- tries obtained from RDKit (Landrum) 2013). This approach allows us to capture the diverse 3D) characteristics of each molecule across its various conformations.
Energy Distance(ED). Energy Distance is statistical measure used to quantify the difference be- tween two probability distributions. It is based on the concept of energy statistics and captures the distance between distributions in terms of expected Euclidean distances between samples. Energy Distance is symmetric and particularly sensitive to both location and shape differences between dis- tributions. In this study, we compute the Energy Distance between the distribution of the generated molecules and the distribution of OSDAs reported in the literature, using the WHIM descriptors. The formula for Energy Distance is given by:
ED(X, Y) 2 E(d(X, Y)] - E[d(X, x B[d(Y,
where X and Y represent independent samples from the generated molecules and OSDA distribu- tions, respectively. Here, d(X, Y') denotes the Euclidean distance between samples, and denotes the expectation. The Energy Distance captures both the mean differences between the distributions and their internal variability, making it robust measure for comparing the overall structure of the molecular distributions.
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KL-divergence. Kullback-Leibler (KL) divergence is asymmetric difference between two probability distributions. It measures the information loss or gain when one distribution is compared to another. Here, after dimensionality reduction via PCA, to calculate the difference hetween the distribution of the generated molecules and the distribution of OSDAs reported in the literature. The formula for KL- divergence is:
used to quantify the
the WHIM descriptors of the molecules,
P(a) ((x) dx.
DKL (kl(()))) - P(x)) log
where P(x) is the probability density function the generated molecules' distribution, and Q(x) the probability density function of the OSDAs' distribution. The KL-divergence quantifies the amount of information lost when Q is used to approximate P, with a larger value indicating greater divergence between the two distributions.
Scree Plot of PCA
Expla ned Variance
Cumulative Explained Variance
0.8
Explained Variance Ratio
0.6
0.4
0.2
0.0
2
3
5
8
10
11
12
13
14
15
Principal Component
Figure 6: Scree plot of PCA on WHIM descriptors.
Table 2: Screening criteria
Property
Criteria
Number of rotatable bonds
Less than 5
Size of rings without bridge bonds
Smaller than 9-membered rings
Size of smallest rings
Larger than 4-membered rings
Distortion of N-rings to N ratio
Must not contain triple bonds in N-rings
More than 4 and less than 20
Presence of NH and NH;* group
Must not be contained
Must not contain these atoms and bonds
Presence of unfavorable atoms and bonds
(O, Si, S. B, -0-C-0-, OC and N N-N)
D USING THE FEW-SHOT COT (CHAIN OF THOUGHT) PROMPT ENGINEERING METHOD FOR OSDA MOLECULE DESIGN CASES
In this section, we present the complete prompt that used.
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Chain of Thought
Let's think step by step design an effective OSDA molecule for given zeolite structure. We will follow these steps:
(1)Analyze the Target Zeolite Structure: Begin by reviewing the key features of the target zeolite, including its pore size, topology, and framework type. Consider how these structural characteristics might influence the binding and stability of potential OSDA molecules.
(2)ldentify Functional Groups: Based the analysis of the zeolite structure, identify functional groupe that are likely to enhance the interaction with the zeolite framework, Consider groups that can fit well within the pores and stabilize the structure.
Anyyne Example Molecules: need to analyze the given examples understand the relationship between the SMILES structures and their binding energies and synthesizability scores. This analysis will help guide the selection of structural features in the design of new OSDA molecules.
(4)Generate SMILES for OSDA Candidates: Using the identified functional groupe, construct series of SMILES representations for potential OSDA molecules. Ensure that these molecules are chemically valid and capable of directing the assembly of the zcolite structure.
Figure 7: Chain of Thought prompts in OSDA design.
Chain of Thought prompts
You chemist tasked with designing Organic Structure Directing Apents (OSDAs) for AFI Zeolite. Your goal is to generate OSDAs with spectfic binding energies and synthesizab scores. Below are several examples, including the SMILES strings of the OSDAs, their binding energies, and synthesizability scores. instario, the SMILES string C1C@@@/11C2C00441220000022 synthesizabliity score 1.827.
Relevant Context:
binding energy -7.101 kcal/mol
Another example, NCCONCCNGOON, has binding energy of 7.500 kcal/mol and synthesizability score of 1.818. a binding energy -6.961 kcal/mol
The SMILES string synthesizebliity score 1.879. Finally, the structure hawaaaaaaaaaaaaaaaaa//////////// and synthesizabillty
has a binding energy of -8 199 kcal/mol are preferred.
2.544. Lower binding energies and synthesizebility
Question: Can you dosign new OSDA of AFI Zoolito and provido its SMILES string? You ust need to provide SMILES.
Thought: Let's think step by step design effective OSDA
given zealite
We follow
these steps:
(1) Anulyze the Targel Zeolite Struclure. Begin reviswing the key fealures of the largel ceolite. including its pure sizu, topology, and framework type. Consider how these structural character istics might influence the binding and stability of potential OSDA molecules.
(2) Identify Functional Groups: Based on the analysis of the zeoite structure, kdentify functicnal groups that are likely to enhance the interaction with the zeolite framework. Consider groupe that can wall within the pores and stabilize the structure.
(3) Analyze Example Molecules: need to analyze the given exemples to understand the relationship between the SMILES structures, their binding energies, and synthesizebility scores. This analysis w help guide the selection of structural features the design of OSDA moieculles,
(4) Generate SMILES for USDA Candidates: Using the identified tunctionll groups, construct series of SMILES representations for potertial OSDA moleculss, Ensure these molecules are chemicellyy valid capable directing the essembly of the zeolite siructure.
Action: Frrish(petec(C))p-CCCyyy)
Figure 8: Details of the few-shot COT (Chain of Thought) prompts. The data in the Relevant Context section comes from the OSDB database.
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Reflection prompts
Relevant Context:
You are chemist tasked with deskgning new Organic Structure Directing Agenta (OSDAs) for AFI Zeolite. Your goel is to generate OSDAs with specific binding energies and synthesizab lity soores, Below are several examples, including SMILES strings
binding energies, synthesizabillty scores. For instance, the SMILES string ([ synthesizabliit soore 1.927.
has binding energy of -7.11 kcal/mol and
Another example, NCCONCCNCCON, The SMILES string (CCCCCG)) hCCcc/CCC@aa1CacCCCcC..Coooo www.a//0ccccccccccccccccccc has binding energy of -0,961 kcal/mol and synthesizaalilly score 1.879.
binding emergy -7.500 kcal/mol syntheaizabliity score 1.818.
Finally, the structure and a synthesizabliity score of 2 2.544, Lower binding enargies and synthesizabillly scores are preferred.
binding anergy 199 kcalmmol
Question: Can you design new OSDA Zeolite and provide SMILES string? You need to provide SMILES.
Thought: Let's think stop by step to design an effeclive OSDA malecule for given zealite structure. We will follow
these steps:
Analyze Target Zeolite Structure: Begin by reviewing the key features target zeolite, including its pore size, lopolugy, framwwork lypu. Consider how Ihuse structural characler istics might influencc thu binding and stability potantial OSDA molecules.
Identify Functional Groups: Besad the analysis zebiite structure, identify functional groups are likely enhance the interaction with the zoolite framework, Consider groups that fil wall within the and stabilize the structure.
Analyze Example Molcoules: need to analyzo given examples understand the relationship betweon SMILES structures, their binding energies, and synthesizebillty scores. This analysis Il help guide selection of siructural features in the design of naw OSDA molecules.
groups, representations for potertial OSDA molecules. Ensure that these molecules chemically valid and capable directing the assembly of the zeolite structure.
Generate
Candidates: ng identifled
Action: Firiah[Cotca(Cle==][Chhth
Reflection:
My included SMILES string that
correct
should that
SMILES strings are accurately represented conform standard notation avoid similar issues.
Figure 9: Details of the reflection prompts. The data in the Relevant Context section comes from the OSDB database.
Self-reflection
You have atlempted to answer following question before and failed. The following reflection(s) give plan to avoid failing to answer the question the same way you did previously. Use them to improve your strategy of correctly answering the given question.
Reflections:
-My previous attempt design new OSDA zeolite failed generated SMIL ES string was too high. This indicates that the molecule too difficult to synthesize. Additionally, the binding energy not optimized to be low possible.
the synthesizabillty score the
-My previous attempt failed because the generated SMILES string did not meet the empirical characteristics of OSDA molecules, specifically the nitrgee-ttoccaboo ratio and the structural requirements for rings and rotatable bonds.
Actor
Thought:
Based the reflactions and the given context, it's clear that the previous attempts failed due to high synthesizabillt scores and not optimizing the binding energy. To avoid these issues, need to design malecule that is simpler to synthesize and has lower binding energy. The given examplas show that molecules with nitrogen and carbon atoms, as well as ring structures, tend to have lower binding energies and synthesizabilly scores. Therefore, should aim design molscule with these characteristics. Additionally, should aim keep the molecule simple as possible ensure lower synthesizabillty score.
Figure 10: Example of the reflection.
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E BINDING ENERGY ESTIMATION MODEL DETAILS
This section covers the details and results of the Binding Energy Estimation Model, including our ablation study on the model.
The OSDA-Zeelite complexes in the real world quite complex, OSDAs may adopt multiple conformations when interacting with zeolites, resulting in complexes that do not exhibit the strict periodicity of typical crystals. However, both the complexes from the OSDB database and those generated from voids are stored as CIF files and treated as unit cells, as illustrated in Figure [T Therefore, we continue to use erystal structures and periodic graph neural networks (CGCNN) modeling in this context.
(a) AFI
(b) CCN(CC)CC-AFI Complex
Figure 11: Dock illustration. (a) An AFI framework zeolite. (b) The combined complex with the
"CCN(CC)CC" OSDA.
Table 3: Binding energy prediction error (MAE) the OSDB database.
Model
Binding Energy (k.J/mol Si) (MAE L)
full model
0.384
Remove Zeolite encoder
0.402
Remove Smiles encoder
0.411
Only Complex encoder
0,469
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Text Regressn Line
-00
Test
Figure 12: The results of the Binding Energy Estimation model on the test set.
Training Staps (Original Smoothed)
14
Onggnn Traniing Lass Smoothed Turrnng
0.0
0.
30000
20005
30200
40000
52020
Training Steps
Figure 13: Model loss trends.
F SUPPLEMENTARY EXPERIMENTAL RESULTS
This section primarily provides supplementary results from the experiments.
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PCA Analysis: OSDAS for LTA
ALL
Literature Generated
Literature Generated
30.0
PCA
PCA
0.-
PCA
PCA
Figure 14: Principal Component Analysis (PCA) of WHIM vector representation for OSDA molecules used in LTA framework. PCA 1, 2, and 3 represent the first three principal axes. Red points show OSDAs generated by the OSDA Agent, blue points represent OSDAs of L.A frame- work from the literature, and gray points represent all OSDAs extracted from the literature.
Proparty Distr ibtiinn OSDAs
Uie
lephecicty
100
4.0
400
200
00
-02
feent
0600-ggert
Usenzure
2006 3006.pget
Property Distributions: OSDAs for LTA
Weight
Volumm
Auphenciyy
800
200
200
-23
Lawzbee
0404 Ageit
GFT4
G914
GF14
Figure 15: The distribution of molecular volume, molecular weight, and asphericity is compared between the OSDAs reported the literature and those generated by the OSDA Agent in conjunction with GPT44
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Property
Wel A
Vellmee
Asphicicity
-23
LF1-4
Linober ANDRAMY
GP14
1441
6413
U114
IMD
Propely Distributions: OSDAs for LTA
Vollme
Arphericity
379
200
200
-22
Lteutae: 0504 Ammm:
CFT-
Huto
86755
Lteratae C524.o0
CPT4
Uievetar 0604
GP7.
Figure 16: The distribution of molecular volume, molecular weight, and asphericity is compared between the OSDAs reported in the literature and those generated by the OSDA Agent in con conjunction with GPT-4, BioT5, MolT5.
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Scacore 3,489 6
Soasone Es
Soscone 1.334
Scazzee Ep
141
Scxcoee 822 Eg
Sescore 3420 Rp 69
2,805 F -5.75
Sescore Eg -8.10
2.572
Scacore Rp -8.01
2.735
1,161
& -8.01
Scacire 2 Fg -334
Soacons Eg 8.81
3,278
Scacore 2,830 Ep -7.75
Scacore 332 F 7
Scecare 8.15 -7.95
Szscore 1.98
Scsocre 1.84
Sascore Fg 8.44
1.88
75
Scscore 3.102 Eg
Scsoore
Soscere 2,008
Scosore 3.16
Sceoore 2.55
Scccore 2.01 Es -7.79
Scecore 2.00
Scscern
E, -7.30
6, -6.5
Es 8.90
Eg -5.16
Figure 17: The optimization results of OSDA molecules presented, with the OSDA molecules from the OSDB database on the left and the molecules optimized by the OSDA Agent on the right.
G ABLATION STUDY
In this section, perform ablation study the proposed components, focusing the reflection mechanism and the individual components within the Evaluator. To minimize randomness as much as possible, we use 15 identical sets of prompts design OSDA molecules for AFI framework. The results obtained are as follows:
Table Performance metrics for the ablation study of different methods.
Method
Validity
f
Morgan
MACCS
RDK
Divergence
Avg Rank
OSDA Agent
1.000
0.601
0.368
0.816
0.623
0.934
0.825
w/o reflection mechanism
0.702
0.581
0.331
0.782
0.553
1.359
0.973
4.75
w/o RDKit
0.770
0.593
0.355
0,751
0.566
1.233
0.830
w/o SCSore
1.000
0.570
0.371
0,802
0.614
1.256
1.001
2.85
w/o blending energy
1.000
0.627
0.356
0.787
0.619
1.275
0.972
2.42
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Here, observe that removing the reflection mechanism any individual component within the Evaluator leads to worse results.
H EVALUATION OF ALTERNATIVE LLM COMPONENTS
In this section, explore the of alternative large language models (LLMs) and compare their performance. Specifically, we tested two popular open-source LLMs, Mistral (Jiang et al. (023)) and LLaMA 3.1 (Dubey et al.) 2024), and observed that our method also resulted in substantial improvements in their ability to design OSDAs:
Table 5: Performance metrics for different methods.
Method
BLEU f
Morgan f
MACCS f
RDK
OSDA Agent
0.601
0.368
0.816
0.624
OSDA Agent"
0.571
0.317
0.772
0.601
Ilama
0.522
0.301
0,628
0.416
OSDA Agent(llama)
0,551
0.315
0.754
0.565
Mistral
0.338
0.177
0.411
0.224
OSDA Agent(Mistral)
0.512
0.306
0.740
0.541
The OSDA Agent is our default model. The OSDA Agent* replaces the actor with GPT-4o while the OSDA Agent (Llama) is fully built Llama, the OSDA Agent (Mistra) is fully built on Mistra. Our experimental results show that, regardless of the model used, our OSDA Agent significantly enhances the overall design results. However, currently, its performance is still slightly inferior to GPT-4, which remains the most effective model for this specific task.
1 EXAMPLES OF OSDAS FOR AFI, AEI, LEV, AFX, MOR, LTA AND ITE ZEOLITES GENERATED USING THE OSDA AGENT
We used the OSDA Agent to generate OSDA molecules for seven types of zeolites, including AFI, AEI, LEV, AFX, MOR, LTA, and ITE. After consulting experts in materials and chemistry, they confirmed that the generated molecules have potential as OSDAs.
Table 6: Overview of the zeolites utilized.
Framework Type
Crystal system
Length (A)
Longth (A)
Length (A)
Angle
Angle
Angle
pore size (A)
LTA
Cubie
11.92
11.92
11.92
90
90
90
4.1
AFT
Hexagonal
13.83
13,83
8.58
90
90
120
7.42
ITB
Orthorhombie
20.75
9.80
20.01
90
90
4.21
LEV
Trigonal
13.17
13.17
22.58
90
90
120
3.53
Orthorhombic
AFX
Hexagonal
13.67
13.67
19.70
90
90
120
3.73
AEI
Orthorhombic
13.68
12.61
18.50
90
90
3.84
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can EXcO000a
SMIES CMMEECEAANROOOOOOOOOO
MALL CHABOOONOOOOOOOOOO
SAILD OCCOKOROONN B0 (0000000
SANLS WOOKCUEEEECOOOOOOOOOOOOOOO
SM
Figure 18: OSDAs for AFI framework
DAISS CANEAEENOON
WAIS www.
SMIPS OM
DAIES CONEENIIOON
SHESS ONDDOCTEEEN Www.OCCEEEOTTTTTOO
Figure 19: OSDAs for AEI framework
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Misi sprctcceeniiccco cccaup souchheeos
SMALE CSO0
VOCADOO
SALEX SAPTAAOOHINCCC
PALLS COOOOOO
SALIS WWW.OIOOOOOO
Figure 20: OSDAs for AFX framework
OMISS www.arcccccooooooooooooo
GHESS YESPEATE portooeeee
MARS CON www.n/oooooeeeeeeoo....
SHEAS ccw.stootipeesseeeeee
Figure 21: OSDAs for ITE framework
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HyN
SMICE CHOOOOO
SX000C
NH
JMLSS OCONN XOXO
JALLS COONHOOOOK
SHILS ON ROCXXXOC
JAME CIOMMNCIIO
SING OHHOOOOK
Figure 22: OSDAs for LEV framework
SuIrS www.ooooooooooo
GALS www.pootoooooeeeerrr
SAITS www.wooo......ooo.oo.oo.o
CHETS poorIAAMET
UMAN CA-JOICAAEREEE
SIBAA www.BBBISAAAOOOO.....
MARA car.jea
Figure 23: OSDAs for LTA framework
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COMDOOO
SMIDD DON 100000
CONOOC C0H00N
SHILL COOOMOOOOOOO
SHELS COMINOOORONEE
MUS CON-DOOOOOOON
SALES COMINOXXAANOOO
MULE WOW. POOEN
Figure 24: OSDAs for MOR framework
J DEFINITION AND EXPLANATION OF TERMS AND CONCEPTS IN THE PAPER
SMILES (Simplified Molecular Input Line Entry System)
SMILES is symbolic language used to represent the structure of chemical molecules. It encodes the atoms, bonds, and spatial arrangements of molecule used in computational chemistry and Al for Science due to its simplicity in storing and exchanging chemical information and its ability be parsed by computer programs.
string of characters. SMILES is widely
In-Context Learning (ICL)
In-Context Learning refers the process where pre-trained language model performs reasoning and generates responses based on provided examples or contextual information the input. This done without the need for additional model training parameter updates, making it form of immediate inference for a given task.
Chain of Thought
Chain of Thought refers reasoning process where model breaks down complex problem into sequence of logical steps to improve the accuracy and transparency of its reasoning. This approach helps in step-by-stey mroblem-solving by guiding the model through intermediate steps.
RDKit
RDKit is an open-source toolkit for cheminformatics that provides wide range of functionalities for molecular manipulation, feature extraction, structure visualization, and drug design. It widely used in chemistry and bioinformatics.
SCScore (Synthetic Complexity Score)
SCScore a metric used to assess the synthetic difficulty of chemical molecule. It considers factors such the number of synthetic steps, reagents, and reaction conditions required to synthesize the molecule, evaluating its complexity from laboratory synthesis to industrial production. A higher score indicates greater synthetic difficulty.
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Zeolites
Zcolites are a class of porous materials with a regular framework structure composed of silicon- oxygen (SiO4) and aluminum-oxygen (AIO4) tetrahedra linked by oxygen bridges. Their unique molecular sieve properties allow them to selectively adsorb and separate molecules based on size and shape. Zeolites are widely used in various industries for applications such catalysis, adsorption, ion exchange, gas separation, and water treatment. Due to their tunable structure, the pore size and crystal morphology zeolites can be optimized by using different organic structure-directing agents (OSDAs) to meet specific application requirements.
OSDA
OSDA (Organic Structure-Directing Agent) refers to an organic compound, typically a quaternary ammonium salt or other organic molecules, that is used in the synthesis of zeolites to guide the formation of their crystalline structure. During the synthesis process, OSDAs interact with the aluminosilicate framework and help direct the arrangement of atoms, thereby influencing key char- acteristics the zcolite such as pore size, pore shape, and crystal morphology. The use of different OSDAs allows for the tuning of the zeolite's 's properties to suit specific industrial applications, such as catalysis, adsorption, and gas separation.
VOID
VOID (Voronoi Organic-Inorganic Docker) is tool for molecular docking and materials design, especially for nanoporous materials. It uses Voronoi diagrams to identify docking sites in crystal structures and combines geometric fitness functions and Monte Carlo methods to optimize docking. Supporting batch docking with tensor operations for efficiency, VOID is widely used in materials science and molecular modeling.
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Reviews:
[{"review_id": "klJcHMrBEg", "reviewer": "Reviewer_93jd", "summary": "The paper introduces the OSDA Agent, an innovative framework combining Large Language Models (LLMs) with computational chemistry tools for the de novo design of Organic Structure Directing Agents (OSDAs) for zeolites. The framework includes three main components: Actor, Evaluator, and Self-reflector, enhancing the process of generating and optimizing OSDA molecules. Demonstrates significant improvements over existing models in generating OSDAs that are not only theoretically valid but practically feasible. Provides a comprehensive methodological approach and substantial experimental results, setting a new standard in computational chemistry and molecular design.", "questions": "1. Can the methodologies and framework presented be adapted to design other types of molecules or materials not discussed in the paper? What changes or adaptations would be necessary? \n2. What are the computational resource requirements for implementing the OSDA Agent framework, significantly when scaling to larger datasets or more complex molecular structures? \n3. How does the framework handle changes or updates in computational chemistry tools or LLMs? Is there a mechanism to easily integrate new tools or updates in knowledge of the field? \n4. Could you elaborate on the choice of evaluation metrics used? How do these metrics align with the practical requirements of OSDA applications in the industry?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Integrating LLMs with computational chemistry tools to create a feedback-informed molecule generation model is unique. This model addresses the limitations of traditional molecule design models by providing an interactive and iterative design process that includes a novel binding-energy prediction module (Section 4.1). \n2. The experimental design is robust, with clear definitions of metrics and methodological approaches that ensure the reproducibility and validity of results. The use of a layered approach combining different computational tools to assess molecule viability is particularly noteworthy (Sections 5.1 and 5.2). \n3. The paper is well-structured and written, offering detailed explanations of the processes and technologies involved, such as the Actor-Evaluator-Self-reflector framework. Figures and tables effectively illustrate complex processes and results. \n4. This research has a high potential impact, particularly on industries relying on zeolites. Generating and optimizing OSDAs more efficiently could lead to significant advancements in material science and related fields.", "weaknesses": "1. The paper could better address how the proposed model might generalize to other types of molecular design beyond OSDAs for zeolites. It is unclear whether the techniques and improvements reported apply to broader classes of materials or chemical processes. \n2. The reliance on external computational chemistry tools may introduce limitations related to the scalability and speed of the proposed approach. The paper could expand on the implications of these dependencies, particularly in terms of computational cost and accessibility. \n3. The models are trained and validated primarily using the Zeolite Organic Structure Directing Agent Database and the Jensen dataset. The diversity and representativeness of these datasets could be questioned, potentially affecting the robustness and applicability of the findings (Section 3.1).", "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": "2esYeIWyIr", "reviewer": "Reviewer_bUa4", "summary": "This paper introduces OSDA Agent, a novel framework that combines LLMs (particularly GPT-4) with computational chemistry tools to design Organic Structure Directing Agents (OSDAs) for zeolite synthesis. The framework consists of three key components:\n\n1. An Actor powered by GPT-4 that designs potential OSDAs using few-shot Chain of Thought prompting, In-Context Learning with OSDB database examples, and a memory component for context.\n\n2. An Evaluator that employs computational chemistry tools (RDKit for validity, SCScore for synthesis feasibility) and a novel binding energy model(having an MAE of 0.384 kJ/mol Si) to assess generated structures. \n\n3. A Self-Reflector using GPT-4o that provides iterative feedback based on evaluations stored in memory to improve chemical validity, synthetic feasibility, and binding energy estimations through a generation-evaluation-reflection-refinement workflow.\n\nThe authors validate their framework across multiple zeolite types, demonstrating its ability to generate chemically valid OSDA candidates outperforming the baselines across multiple similatiy metrics. Additionally, the framework successfully optimizes existing OSDA molecules, reducing their synthetic complexity scores from 3.45 to 2.46 while maintaining their functional properties and keeping binding energies within desired ranges (-3.38 to -9.00 kcal/mol). The authors benchmark their experiments against other methods such as MolT5 and BioT5 using diverse metrics including validity, MACCS, and BLEU scores. Subject matter experts have confirmed that the OSDAs designed by the OSDA Agent show the potential to function effectively as structure-directing agents.", "questions": "- Could you specify the number of iterations of the OSDA agent (design-evaluate-reflect iterations)? What criteria did you use to determine the optimal number of iterations?\n\n- The paper demonstrates OSDA Agent's success on multiple zeolite types, how does your framework handle increasing zeolite complexity? Are there any known limitations in terms of zeolite structure complexity or OSDA size?\n\n- Could you elaborate on the expert validation process? Specifically, what validation criteria were used, and have any of your generated OSDAs been experimentally synthesized?\n\n- Your framework integrates multiple components (reflection mechanism, multiple aspects in the evaluator such as RDKit, SCScore, and binding energy models). Have you conducted ablation studies to understand their relative importance? For instance, how much does the reflection mechanism contribute to the final performance, and what are the most common types of failures?\n\n- What motivated the choice of using different LLM variants (GPT-4 for the Actor, GPT-4o for the Self-reflector) in your framework? Have you conducted comparative experiments with other LLM combinations, either using the same model for both components or testing with open-source LLMs?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Novel OSDA framework that combines LLMs with chemical validation tools and binding energy models.\n- Immense practical significance due to zeolites' widespread industrial applications, making efficient OSDA generation methods valuable.\n- Strong experimental results demonstrating OSDA Agent's ability to generate competitive and sometimes superior OSDA designs over baseline methods such as MolT5, BioT5, and pure GPT-4\n- Comprehensive evaluation using multiple metrics (validity, similarity, distribution measures) strengthened by validation from domain experts.\n- Demonstrated capability to optimize existing OSDA structures by reducing synthetic complexity scores while maintaining functional properties.", "weaknesses": "- Additional experiments examining the relative importance of different components (e.g., the reflector mechanism) and various tools (RDKit, SCScore, binding energy models), along with analysis of failure patterns (such as consistent failures in chemical validity) would strengthen the paper's findings.\n- The paper does not specify the number of iterations used in the design-evaluate-reflect process.  Additional analysis of how metrics change with iterations and when the performance plateaus would be valuable.\n- A comparison with domain-specific fine-tuned LLMs or using LLMs other than GPT-4 could strengthen the generalizability of the work.", "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": "Vr3U4dg2Ti", "reviewer": "Reviewer_t8Dz", "summary": "This paper introduces the OSDA Agent, an interactive framework specifically made for designing organic structure directing (OSDA) agents, which is critical for zeolite synthesis. The main goal of the OSDA agent is to generate the de novo design of OSDAs with given zeolites as the target. This framework integrates computational chemistry tools into LLMs to check the quality of generated molecules and includes a reflection mechanism to improve OSDA generation. The OSDA Agent proposed in this work contains three key components: actor, evaluator, and self-reflector. This novel framework design creates a continuous learning environment that would facilitate more efficient and effective zeolite synthesis planning. Experimental results from the paper demonstrate the OSDA Agent yields better quality results compared to existing baseline models.", "questions": "1. Robustness of the input to the agent model: as there are multiple ways to represent a chemical, does this model support multiple chemical expressions, i.e. chemical formulas like H2O and IUPAC names like oxidane? The 'Evaluator' in the agent model would convert the input target zeolite into a SMILES string, but have you considered changing the chemical expression to see if the 'Evaluator' still works? \n2. Accuracy of binding energy estimation model: this work proposes a new binding energy estimation model to lower the computational complexity of previous computational tools, but how accurate is this estimation model, and is the estimation from this model comparable to traditional atomic simulation methods?  \n3. LLM consideration: this work considers OpenAI's GPT models as the base LLM for the agent, and they are not open-source models and would be potentially costly to use, is there any reason that the authors did not also consider open-source models like Llama and Mistral, or would you consider experimenting with those open-source models? \n4. Performances of the baseline models: curious about performance compared to baseline models, specifically for the 'validity' metric the authors proposed, how is this calculated, is it based on results from multiple computational models? This question stems from the result showing that one of the baseline models has a validity score of 0.00 for both datasets whereas the proposed model has a score of 100. \n5. Memory and cost: this agent model includes a reflection mechanism and the paper also shows the number of reflections vs SCScore plot, showing that 4 reflections would reduce the synthesis difficulty, would having more reflections yield even better performances, and what is the cost of reflections?\n6. Safety concerns: do GPT models always fulfill users' requests, and is there any case that the model refuses to fulfill the request or gives out a warning due to safety concerns of the generation process or the chemical itself, or is there any case that the model fails to generate the answer due to its limited knowledge of certain uncommon chemicals?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. Novel chemistry LLM agent for molecule generation with a reflection mechanism design: the self-reflection modules in the OSDA Agent reuse feedback from previous iterations to optimize future trials, and this enhances the agent's decision-making ability effectively. \n2. Tools to evaluate the quality of generated molecules: this model also includes three chemical tools to check if the generated chemical is valid and feasible, by checking the validity with RDKit, synthetic complexity. To estimate the binding energy, the authors also train their own binding energy estimation model that has lower computational complexity compared to existing computational methods.", "weaknesses": "1. Limited post-generation check: this agent framework includes three chemical property checks, and those would be useful for checking if the generated chemical is valid or reasonable, however, it does not consider other properties such as toxicity and explosiveness, the safety of the chemical in general. \n2. see questions below", "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": "37Ld9zTMvC", "reviewer": "Reviewer_WsXS", "summary": "The authors introduce OSDA Agent, an interactive framework for designing Organic Structure Directing Agents (OSDAs) used in zeolite synthesis. The framework leverages large language models (LLMs) as the core intelligence, complemented by computational chemistry tools. OSDA Agent consists of three key components: the Actor (generates potential OSDA structures), the Evaluator (assesses generated OSDAs using computational tools), and the Self-reflector (produces reflective summaries to refine subsequent outputs).\nThe main advantages of OSDA Agent are:\n\n- Improved generation quality compared to pure LLM models, producing candidates consistent with experimentally validated OSDAs.\n- Integration of chemical knowledge and tools to ensure generated molecules adhere to chemical rules and are feasible.\n- Interactive and iterative design process that leverages feedback and self-reflection\n\nExperiments demonstrate that OSDA Agent outperforms baseline methods, including state-of-the-art text-based de novo molecule generation approaches.", "questions": "1. Can you provide more context on why OSDAs pose such a challenge to contemporary methods?\n2. What specific aspects of OSDA design call for an interactive, feedback-driven approach like the one you've developed? Are there certain subclasses of OSDAs that current methods struggle with?\n3. Can you provide an overview of the working of the whole OSDA framework descriptively, without pointing to existing work?\n4. How does this work contribute to the broader fields of representation learning or algorithmic design beyond an application of existing methods to OSDA synthesis? Can you elaborate on the novelty of your approach compared to existing frameworks?\n5. How does the binding energy estimation model work, and why is it crucial for your approach? Could you explain this concept in more accessible terms?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "[Algorithm use] The work seems to highlight the successful use of existing LLM pipelines in searching a vast space of objects of scientific discovery. In this case, the objects were chemical agents, OSDAs, but theoretically, these could also be molecules, drugs, equations, etc. It also presents the possibility of connecting LLM pipelines to more traditional algorithms or solutions which allow evaluating the outputs of LLM models and sending feedback to improve future iterations. This is valuable for the community since it shows the viability of prior work.\n\n[Results] Building on the previous strength, the use of the existing methods allowed authors to discover researched OSDAs as well as potentially new ones. This shows the robustness of the framework, possibly opening new avenues to zeolite synthesis. The real consequences of the introduced method are difficult to predict without expert knowledge of the chosen topic.", "weaknesses": "[Motivation] Being a non-expert in chemistry it is difficult to grasp why OSDAs pose such a challenge to contemporary methods. Linked to that, the description of related work could use some more detail, at least 1 example of an existing method and the way it works with a discussion on how the method presented in this work builds upon its weaknesses. The authors do try to convey that new algorithms for OSDAs are needed but it isn’t clear to me why. Is it only the need to give feedback to the algorithm in order to get better outputs with the next iteration (impossible to achieve with traditional ML methods)? If so, why would it be so crucial for OSDAs? Is it that the current methods are ineffective for some subclass of OSDAs or…?\n\n[Specificity] The work may be certainly worthwhile to OSDA specialists but I have doubts whether it would be of general interest. It shows how to apply an existing framework to a specific task of chemical agent construction. There seems to be limited novelty when it comes to representation learning itself or algorithmics in general. The reflection mechanism that verbalizes errors found by the Evaluator for future learning seems to be taken from a paper by Shin et al. (2024), and adjusted for this particular task, similarly to other used algorithms.\n\n[Clarity] As much as the manuscript is written well and it is possible to understand the syntax and often what is being conveyed, some of the jargon becomes weary and makes details intelligible. This happens in Section 3, where the data is described and seemingly one of the crucial contributions of the work — the binding energy. It is also evident in Section 4.1 —the authors talk about SMILES sequences, In-Context Learning, Chain of Thought, etc. without ever defining, even intuitively, what these acronyms convey. See also RDKit, SCScore, etc. Section 4.1. would benefit from explaining intuition on the framework rather than an overview with specifics defined later.\n\n[Details] There are a lot of moving parts in the whole framework but there is little detail on how they work exactly. Which of the parts are new knowledge? How does the whole framework operate, from start to finish? Each step in the processing could be described in a separate subsection, to streamline presentation.", "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 (Spotlight)