Title: Integrating Drug Substructures and Longitudinal Electronic Health Records for Personalized Drug Recommendation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Drug-Drug interaction; Drug Recommendation; Drug Substructures
Abstract: Drug recommendation systems aim to identify optimal drug combinations for patient care, balancing therapeutic efficacy and safety. Advances in large-scale longitudinal EHRs have enabled learning-based approaches that leverage patient histories such as diagnoses, procedures, and previously prescribed drugs, to model complex patient-drug relationships. Yet, many existing solutions overlook standard clinical practices that favor certain drugs for specific conditions and fail to fully integrate the influence of molecular substructures on drug efficacy and safety. In response, we propose \textbf{SubRec}, a unified framework that integrates representation learning across both patient and drug spaces. Specifically, SubRec introduces a conditional information bottleneck to extract core drug substructures most relevant to patient conditions, thereby enhancing interpretability and clinical alignment. Meanwhile, an adaptive vector quantization mechanism is designed to generate patient–drug interaction patterns into a condition-aware codebook which reuses clinically meaningful patterns, reduces training overhead, and provides a controllable latent space for recommendation. Crucially, the synergy between condition-specific substructure learning and discrete patient prototypes allows SubRec to make accurate and personalized drug recommendations. Experimental results on the real-world MIMIC III and IV demonstrate our model's advantages.  
The source code is available at \href{https://anonymous.4open.science/r/DrugRecommendation-5173}{https://anonymous.4open.science/}.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose a novel drug recommendation approach, SubRec, which is inspired by the common clinical practice of referring to a patient’s past diagnoses when prescribing new medications. SubRec models the relationship between patients and drugs by identifying drug substructures that are relevant to the individual patient. It then learns a discrete representation of these substructures to effectively recommend new drugs.\n\nSTRENGTHS AND WEAKNESSES:\nstrength:\n\n- The paper is well-motivated and addresses the important challenge of providing explainable diagnoses using machine learning methods.\nDiagnosing many diseases often relies on understanding a patient's medical history and previously prescribed medications, and incorporating such data into drug recommendations seems like an interesting research direction.\n\n- The proposed method models drug substructures and their relationships with various patient-level information to support this process.\n\n- The authors also conduct a sensitivity analysis to examine how individual hyperparameters influence the model’s overall performance, which will be beneficial in replicating the results. \n\nweakness:\n-  While the authors address an interesting problem in explainable drug recommendation, the proposed approach offers limited novelty. The use of longitudinal patient history has been extensively studied, and discrete representations have also been explored in prior drug recommendation research [1]. The authors should more clearly articulate their specific methodological contributions and the overall impact of their approach.\n\n\n- The paper leverages past diagnoses, procedures, and medications to model patient-specific records and learn relevant drug substructures. While this information is indeed important, there is no clear evidence that the learned embeddings capture the critical information necessary for effective drug recommendation, as suggested in Figure 1. A more comprehensive explainability analysis is needed to demonstrate that the embeddings have learned meaningful representations aligned with the proposed hypothesis and have correctly identified relevant substructures in the graph.\n\n- The proposed method appears to be a combination of several existing techniques, relying on multiple assumptions. It remains unclear whether this approach would generalize effectively to real-world clinical settings.\n\n\n- I have reservations about the way the problem is addressed in the paper. Please refer to the questions section for specific concerns. \n\n[1] Wiser: Weak supervision and supervised representation learning to improve drug response prediction in cancer, ICML 2024.\n\nQUESTIONS:\n-  Longitudinal patient records are often highly sparse and contain significant amounts of missing data. Have the authors accounted for this in their method? If not, could they clarify how their approach could be adapted to handle such practical challenges associated with missing data?\n\n- In Line 164, the authors omit a specific loss function that would capture the relevance between the prediction and the encoded representation. What is the rationale for this omission? Wouldn’t this potentially compromise the objective described in Equation 8?\n\n- The objectives described in Equations 8 and 9 are somewhat unclear. Equation 8 aims to minimize the negative conditional mutual information, whereas Equation 9 provides an upper bound in terms of log-likelihood for the same function. Does the objective ultimately require minimizing the conditional log-likelihood? If so, wouldn’t that have a negative impact? Please clarify if there is any misunderstanding on my part.\n\n - What is the motivation for using discrete embeddings in this context? It is unclear what specific problem discretizing the embedding space is intended to solve.\n\n-  Have the authors considered incorporating gene expression or mutation data in their framework for drug recommendation?\n\nLIMITATIONS:\n- The authors should clarify the specific design choices in the losses; please refer to the question section.\n\n- The absence of publicly available code makes it difficult to fully understand  the implementation.\n\n- It remains uncertain whether the proposed method would perform reliably in real-world clinical setting.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nDrug delivery is a prominent research area with significant industrial relevance, and accurate, safe medication recommendation systems have the potential to revolutionize clinical practice. This paper presents a novel framework, SubRec, which integrates molecular drug substructures with longitudinal electronic health records (EHRs) to enable personalized drug recommendation. The authors leverage the Graph Information Bottleneck (GIB) theory to extract core pharmacologically relevant substructures and employ vector quantization to cluster patients based on clinical similarity via a learned codebook, ultimately improving both the accuracy and safety of drug recommendations.\n\nSTRENGTHS AND WEAKNESSES:\n##### Strengths\n\n- The paper addresses a well-motivated and practically significant research problem.\n\n- The proposed approach is conceptually sound and clearly articulated.\n\n- The manuscript is well-organized, clearly written, and logically structured.\n\n- The theoretical formulations are rigorous and provide a solid foundation for the proposed methodology.\n\n\n##### Weaknesses\n\n- The paper lacks a comprehensive analysis of the computational complexity and resource requirements (e.g., training time, memory consumption) of SubRec, which is essential for real-world deployment, especially in clinical settings.\n\n- Additional ablation studies are needed to further isolate the contributions of key components, such as the GIB module, vector quantization, and the patient-clustering mechanism.\n\n- What is the underlying relationship between the CIB-derived subgraphs and the vector quantization (VQ) module? Specifically, how do these two components interact or complement each other in enhancing the representation learning and recommendation accuracy?\n\n- How does SubRec perform when the noise injection mechanism is ablated? This point was not discussied in the paper.\n\nQUESTIONS:\n- Can the authors provide more details on the computational efficiency comparison with baseline models?\n\n- I want to konw how does SubRec perform if the noise injection is removed?\n\n- What is the underlying connection between the CIB-graphs and the VQ module?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper propose a personalized drug recommendation method called SubRec, which innovatively combines drug molecular substructure information with EHR to improve the accuracy, interpretability and safety of drug recommendations. The model proposes two core modules: a key substructure extraction mechanism for CIB to make drug representations highly relevant to the patient's current health state, and VQ for compressing a large number of patient-drug interaction patterns into a discrete prototype space to improve efficiency and enable memory reuse. On the MIMIC-III and MIMIC-IV datasets, the authors demonstrate that the model outperforms multiple existing methods in terms of both accuracy and safety.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The method design is complete, including patient representation, drug substructure extraction, memory mechanism and final recommendation, with clear structure and reasonable module collaboration.\n2. A combination of CIB and VQ is proposed, which helps to improve the degree of personalization and efficiency of recommendation, and has some practical value. \n3.Comparative experiments are conducted on two real medical datasets, covering different categories of baseline methods with rich evaluation indexes.\n\nWeaknesses\n1.\tLimited Technical Novelty: The proposed CIB module aims to extract condition-specific drug substructures, but substructure-based modeling has already been explored in prior work. For instance, SafeDrug [1] and MoleRec [2] both model drugs as combinations of molecular substructures with considerations of drug-drug interaction (DDI) safety. Moreover, the use of vector quantization in representation compression is not new — techniques like VQ-VAE [3] are well-established. The overall design appears to rely heavily on empirical tuning rather than principled innovation.\n2.\tInsufficient Validation of Model Interpretability: While Figure 6 presents a case study on substructure clustering, it lacks expert annotations or supporting biomedical literature, making it difficult to assess whether the extracted substructures are truly pharmacologically meaningful or disease-relevant.\n3.\tLack of Pharmacological Alignment in Substructure Extraction: Although Section 3.2 introduces a substructure extraction strategy based on Graph Information Bottleneck theory, the paper does not provide concrete examples of the extracted substructures nor any explanation of their alignment with known pharmacological mechanisms. This weakens the model’s claim of clinical interpretability.\n4.\tCharts need to be optimized: for example, the background color of “DrugRepresentation and Substructure Capture Modul” in Figure 2 is too light for the reader's intuitive understanding.\n\n[1]\tYang C, Xiao C, Ma F, et al. Safedrug: Dual molecular graph encoders for recommending effective and safe drug combinations[J]. arXiv preprint arXiv:2105.02711, 2021.\n[2]\tYang N, Zeng K, Wu Q, et al. Molerec: Combinatorial drug recommendation with substructure-aware molecular representation learning[C]//Proceedings of the ACM web conference 2023. 2023: 4075-4085.\n[3]\tVan Den Oord A, Vinyals O. Neural discrete representation learning[J]. Advances in neural information processing systems, 2017, 30.\n\nQUESTIONS:\n1. Please identify specific innovations in substructure modeling in this method compared to SafeDrug and MoleRec. If it can be demonstrated that the extracted substructures are more targeted or more relevant to the disease mechanism, it will significantly improve the model interpretation and innovation score.\n2. Lack of citations to relevant literature: \n[1] Enhancing Precision Drug Recommendations via In-depth Exploration of Motif Relationships \n3. Does the specific strategy for constructing the codebook in the vector quantization module shift significantly due to changes in the training set?\n\nLIMITATIONS:\nNA", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 1, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces SubRec, a unified framework that integrates representation learning across both patient and drug spaces. The framework employs a conditional information bottleneck to extract core drug substructures most relevant to patient conditions, thereby improving interpretability and alignment. Additionally, it incorporates adaptive vector quantization to model patient-drug interactions effectively. The proposed approach is validated on the MIMIC-III and MIMIC-IV datasets, demonstrating its efficacy.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The paper is complete, and the results and figures are well-visualized.\n2. The paper is easy to read and follow.\n\nWeaknesses:\n1. Unclear Motivation and Novelty: The motivation behind the proposed SubRec is not sufficiently clear, and its novelty remains ambiguous. The authors have not clearly articulated the differences between the proposed method and prior works such as SafeDrug [39] and MoleRec [40]. Specifically: (1) What are the key limitations of SafeDrug and MoleRec that SubRec aims to address? (2) Why is SubRec expected to outperform these prior methods?\n\n2. Challenges in Personalized Medicine: The authors claim that “SubRec is designed to address two key challenges in personalized medicine: (1) the complexity and sparsity of patient historical records, and (2) extracting patient-specific core substructures.” However:\nThe complexity and sparsity of patient historical records are common challenges in this domain. The authors have not explained why existing methods cannot adequately address these challenges, or how SubRec overcomes them more effectively. This makes the rationale for the proposed method unclear. \n\n3. Graph Information Bottleneck (GIB): The GIB theory referenced in Figure 2 is not explicitly explained. What are the key differences between the proposed method and the following work?\n\n[1] Tailin Wu, Hongyu Ren, Pan Li, and Jure Leskovec. \"Graph Information Bottleneck.\" Advances in Neural Information Processing Systems, 33:20437–20448, 2020.\n\n4. It is unclear how the CIB is integrated into the proposed framework (as shown in Figure 2). A more detailed explanation is required.\n\n5. Lack of Comparisons with Recent SOTA Methods: The paper does not include comparisons with more recent state-of-the-art methods in this field. The following works should be considered for comparison to provide a more robust evaluation:\n\n[1] SeqCare: Sequential training with external medical knowledge graph for diagnosis prediction in healthcare data. Proceedings of the ACM Web Conference 2023, 2819–2830.\n\n[2] Stage-Aware Hierarchical Attentive Relational Network for diagnosis prediction. IEEE Transactions on Knowledge and Data Engineering, 2023.\n\n[3] Beyond Sequential Patterns: Rethinking Healthcare Predictions with Contextual Insights.\" ACM Transactions on Information Systems, 2025.\n\nQUESTIONS:\nI will consider increasing my score if the authors fully address the concerns outlined in the weaknesses section.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 1, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-05"}