Title: Bridging the Gap Between Cross-Domain Theory and Practical Application: A Case Study on Molecular Dissolution
Venue: NeurIPS 2025
Accepted: yes
Keywords: Artificial intelligence in chemical research

gain-based core subset selection

asymmetric molecular interaction graph neural networks
Abstract: Artificial intelligence (AI) has played a transformative role in chemical research, greatly facilitating the prediction of small molecule properties, simulation of catalytic processes, and material design. These advances are driven by increases in computing power, open source machine learning frameworks, and extensive chemical datasets. However, a persistent challenge is the limited amount of high-quality real-world data, while models calculated based on large amounts of theoretical data are often costly and difficult to deploy, which hinders the applicability of AI models in real-world scenarios. In this study, we enhance the prediction of solute-solvent properties by proposing a novel sample selection method: the iterative core subset extraction (CSIE) framework. CSIE iteratively updates the core sample subset based on information gain to remove redundant features in theoretical data and optimize the performance of the model on real chemical datasets. Furthermore, we introduce an asymmetric molecular interaction graph neural network (AMGNN) that combines positional information and bidirectional edge connections to simulate real-world chemical reaction scenarios to better capture solute-solvent interactions. Experimental results show that our method can accurately extract the core subset and improve the prediction accuracy.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a core subset iterative extraction (CSIE) framework for samDesign of light- and chemically responsive protein assemblies through host-guest interactionsple selection to solve the problem of too large theoretical data sets in transfer learning, and on this basis proposes an asymmetric molecular interaction graph neural network (AMGNN) for predicting solute-solvent properties. Through this method, the article can retain a wider chemical space of theoretical data while reducing the complexity of training. The authors fully verified the effectiveness of the proposed core data set selection method and asymmetric interaction network in two scenarios: solute solvent interaction and band gap prediction.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n1. The article has a clear motivation and proposes a novel chemical core subset selection method and molecular interaction network, balancing multiple requirements such as efficiency and overhead, and has good application prospects.\n\n2. The main text and appendix provide sufficient experiments and relatively complete theoretical proofs, and verify the effectiveness of CSIE and AMGNN in multiple scenarios such as solute-solvent reaction and molecular band gap prediction, achieving good results.\n\n3. The article clearly presents the motivation, methods, and results. The content of each section is well-organized and the reading experience is good. The visualization of the methods and conclusions is impressive (Figure 1,3).\n \n\n**Weaknesses:**\n1. The article proposes an asymmetric molecular interaction network, but the discussion on GNN is somewhat thin.\n\n2. Compared with existing subset selection methods based on information theory such as Glister and InfoBatch, the innovation boundary of CSIE is slightly blurred. It is recommended to further emphasize the essential differences and advantages of the method over existing work.\n\n3. Although the author emphasizes advantages such as \"lightweight\" and \"efficient\", there is a lack of clear data support, such as how much training time is saved under what scale of data? How does the memory usage change?\n\nQUESTIONS:\n- Is it possible that CSIE systematically prefers a certain type of “high-information but more difficult to predict” samples, thereby causing overfitting to these areas during training?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces two novel machine learning approaches for modeling solute–solvent interactions: Core Subset Iterative Extraction (CSIE) and the Asymmetric Molecular Graph Neural Network (AMGNN). The authors develop an iterative optimization strategy for selecting a core subset of data points, named CSIE. This method aims to maximize information gain while minimizing redundancy and theoretical bias in the dataset, leading to more representative and efficient training data. To more accurately capture the directional and asymmetric nature of solute–solvent interactions in realistic chemical environments, the authors design a graph neural network AMGNN that incorporates molecular asymmetry into its architecture. Then the authors evaluated CSIE+AMGNN on several datasets.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nThe paper presents a novel subset selection method, Core Subset Iterative Extraction (CSIE), which offers a potentially useful strategy for improving data efficiency in molecular modeling tasks.\n\nWeaknesses:\n\n1.\tLack of Cohesion and Justification: The paper lacks a clear and coherent structure, and the connections between its core contributions—CSIE and AMGNN—are not well articulated. It remains unclear why CSIE is appropriate for the solvation free energy prediction task, and how it integrates with or enhances AMGNN. As presented, the work feels like two loosely connected efforts rather than a unified framework.\n\n2.\tUnclear Motivation for AMGNN Design: The rationale behind the proposed Asymmetric Molecular Graph Neural Network (AMGNN) is insufficiently explained. The paper does not convincingly argue why existing approaches, such as heterogeneous GNNs or simpler methods involving solute–solvent embeddings or interaction labels, are inadequate.\n\n3.\tMarginal Performance Gains: The reported improvements over baseline models are not compelling. In Table 1, AMGNN shows only slight performance gains compared to UniMol and Explainable GNN. Similarly, Figure 4 suggests that CSIE offers only marginal benefits over systematic sampling. This weakens the empirical justification for the proposed methods.\n\nQUESTIONS:\n1.\tMotivation for Dataset Subsampling in Solvation Free Energy Prediction:\nThe paper introduces a dataset subsampling method (CSIE) for solvation free energy prediction, but it does not sufficiently justify why subsampling is necessary in this domain. Are there any domain-specific challenges—such as noise, redundancy, or systematic bias in theoretical datasets—that make subset selection particularly valuable? If not, why not demonstrate the method’s general utility by applying it to widely-used benchmark datasets like QM9 or MoleculeNet?\n2.\tComparison to Full Dataset Training:\nFigure 4 shows that training on the full datasets consistently outperforms all subsampling methods on the Abraham, Compsol, Freesolv, and MNsol datasets. What about on the CombiSolv-QM? This raises a fundamental question: if subsampling does not lead to performance gains, what is the practical benefit of CSIE in this context? This is critical to understanding whether CSIE offers any meaningful advantage.\n3.\tBaseline Comparison for AMGNN:\nThe effectiveness of the proposed AMGNN architecture is not fully validated. Specifically, the paper should include a comparison to a simple baseline where a standard GNN is used with additional embeddings or flags to mark solute and solvent atoms/molecules or just use heterogeneous graph neural network. Without this comparison, it is unclear whether AMGNN provides any substantial improvement over simpler, more interpretable alternatives.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 1, "IMPACT": 1, "ORIGINALITY": 1, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nAddressing the mismatch between large simulated datasets and scarce experimental data, the authors introduce a two-part framework. Core Subset Iterative Extraction iteratively selects a compact, information-rich subset of theoretical solute–solvent data using submodular information-gain criteria computed from pretrained chemical language-model embeddings. The selected subset is then used to train an Asymmetric Molecular GNN that models directional solute→solvent and solvent→solute message passing. Together, CSIE and AMGNN improve experimental-data accuracy and reduce training cost, and the selection strategy generalizes to a second case study on band-gap prediction.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \n1. The CSIE method is grounded in submodular optimization – it iteratively adds samples that maximize information gain, which is a principled way to reduce redundancy in the simulation data. The second component, AMGNN, is well-motivated: real solute–solvent interactions are asymmetric. \n\n2. The paper’s experiments show that combining CSIE + AMGNN yields improved prediction accuracy on real-world dissolution data, indicating the approach successfully bridges the gap as intended. \n\nWeaknesses:\n1. The novelty is split across two techniques, making each part feel somewhat thin in isolation. CSIE draws from known concepts in active learning, and AMGNN is essentially a tailored GNN architecture. The question is if either component by itself is a significant advance. \n\n2. Another weakness is the potential dependence on the quality of the pre-trained chemical encoder used for computing sample diversity – the paper uses a “chemical LLM” to embed molecules for CSIE. If that encoder has biases or limitations (e.g., it might not encode solvation-specific features), CSIE might pick suboptimal subsets. Also, CSIE is evaluated by final model performance, but it would be better to see an intrinsic evaluation: e.g., does the core subset indeed have less redundancy or better cover important solute–solvent feature space than random sampling? \n\n3 The focus is on dissolution; even though they argue it’s a general approach, the paper reads a bit like a case study rather than a broad benchmark.\n\nQUESTIONS:\n1 How sensitive is the CSIE to the choice of the pretrained chemical encoder and the size of subset selected? Also, did the authors experiment with different core subset sizes and how does that affect the final model accuracy? \n\n2 Can the authors clarify the individual contributions of CSIE and AMGNN to the performance gain? Providing an ablation where (a) no CSIE + AMGNN, (b) CSIE + standard GNN, and (c) CSIE + AMGNN are compared on the real data would isolate how much each part helps. \n\n3  The method assumes theoretical data has useful information but includes redundancy. What if the theoretical calculations systematically deviate from experimental reality (e.g., consistently overestimate dissolution free energies or fail for certain classes of compounds)? Would the model still be biased? \n\n4.  The approach was tested on dissolution and briefly on band gap. Could the authors provide more detail on the latter or other domains – for example, did CSIE need any modification for selecting theoretical band-gap data? And does AMGNN’s asymmetry concept apply there (or was only CSIE used)?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a new sample selection framework, Core Subset Iterative Extraction (CSIE), for improving the generalization of molecular property prediction models from theoretical datasets to real-world chemical data. CSIE aims to maximize information gain and reduce redundancy via an iterative EM-like approach using molecular representations augmented with both graph-based and textual features. The work also introduces the AMGNN, an asymmetric molecular interaction graph neural network designed to better capture solute-solvent interactions through directed edge modeling.\n\nSTRENGTHS AND WEAKNESSES:\n## Strength\n1. The paper grounds the CSIE approach in submodular function theory and utilizes information-theoretic and geometric measures for informative sample selection, moving beyond simple uncertainty or loss-based criteria. \n2. The data-centric approaches hold certain reference value in the field of AI for Science, given that this field typically faces the problem of data confounding.\n3. The results show that CSIE enables strong performance even when training on a fraction of the real-world data, and AMGNN outperforms or matches other strong GNN baselines.\n\n\n## Weakness\n1. The use of information gain as defined through average cosine distance in the embedding/HNSW graph is only described in a high-level and is not deeply tied to chemical relevance—there is insufficient empirical analysis or visualization on whether the CSIE-selected core samples are chemically diverse, rare, or cover meaningful classes.\n2. I think that if the CSIEFramework is also applied to demonstrate performance improvements across different network models, it would significantly aid in verifying the method's effectiveness.\n3. Can the authors clarify the attribution of AMGNN's gains over MMGNN or CGIB? Is it driven solely by the asymmetric message passing or also by the use of multimodal (MolCA) representations?\n4. The domain gap issue is important, but the demonstrated improvements are incremental and not conclusively shown to generalize across molecular chemistries or other tasks. I hope to provide some discussion on the generalizability of the method.\n\nQUESTIONS:\nRefer to the weakness.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}