Title: AANet: Virtual Screening under Structural Uncertainty via Alignment and Aggregation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Virtual screening

Drug discovery
Abstract: Virtual screening (VS) is a critical component of modern drug discovery, yet most existing methods—whether physics-based or deep learning-based—are developed around *holo* protein structures with known ligand-bound pockets. Consequently, their performance degrades significantly on *apo* or predicted structures such as those from AlphaFold2, which are more representative of real-world early-stage drug discovery, where pocket information is often missing. In this paper, we introduce an alignment-and-aggregation framework to enable accurate virtual screening under structural uncertainty. Our method comprises two core components: (1) a tri-modal contrastive learning module that aligns representations of the ligand, the *holo* pocket, and cavities detected from structures, thereby enhancing robustness to pocket localization error; and (2) a cross-attention based adapter for dynamically aggregating candidate binding sites, enabling the model to learn from activity data even without precise pocket annotations. We evaluated our method on a newly curated benchmark of *apo* structures, where it significantly outperforms state-of-the-art methods in blind apo setting, improving the early enrichment factor (EF1\%) from 11.75 to 37.19. Notably, it also maintains strong performance on *holo* structures. These results demonstrate the promise of our approach in advancing first-in-class drug discovery, particularly in scenarios lacking experimentally resolved protein-ligand complexes. Our implementation is publicly available at [https://github.com/Wiley-Z/AANet](https://github.com/Wiley-Z/AANet).


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors introduce a contrastive learning-based approach that aims to allow docking with apo and holo structures. They introduce AANet, which uses pockets identified from Alphafold structures and learns alignments with the ground truth holo pockets and experimentally determined apo structures. As a negative loss signal, they use cavities that are not suitable docking candidates. This contrastive learning approach allows their model to exhibit similar performance across apo/holo structures, and they can show that their model learns a similar representation for similar pockets and that their model outperforms current models in almost all areas.\n\nSTRENGTHS AND WEAKNESSES:\nThe manuscript is of high quality, and seems polished, easy to read, and all results are well presented and documented. Their method shows rather consistent improvements across all demonstrated benchmarks. The presented idea to incorporate contrastive learning is not novel per se, but well executed and the specific tri-modal alignment can be beneficial for the community. \n\nThe main weakness I see in this paper is that they do not report any standard deviations or error bars, which makes comparison of some benchmarks difficult. Also, the authors could have compared with more methods and I would like to have seen a more detailed comparison with other related methods, for instance in a related work section. \n\nOverall, the authors show a variety of benchmarks and ablation studies, showcasing the limitations and contributions of their approach well.\n\nQUESTIONS:\n1. The authors say that they use Fpocket to determine potential pockets and randomly select one candidate cavity. I was wondering if the same pocket is selected in each training step (considering the same setup), or if it changes during the training and a new candidate is chosen each time?\n\n2. Do you think your approach could benefit from more augmentations? E.g., randomized positions, different Alphafold structures (maybe from esmfold, boltz1 etc.) \n\n3. The part where the authors introduce training with pocket-agnostic data is not particularly clear to me and I think the clarity of this part could be improved. I am not sure if first, the model is trained with contrastive loss and then fine-tuned with the pocket-agnostic data, or both of them are optimized simultaneously? Also, I was wondering about the specific choice for $\\lambda$. \n\n4. In the manuscript, the authors say that the model is trained \"with all entities overlapping with DUD-E or LIT-PCBA removed\" to avoid data leakage. Similar lines are found in multiple places. I was wondering, whether they accounted for structure similarity, because it seemed like they were only looking at exact matches for IDs. Without structure similarity, this could still lead to data leakage.\n\n5. In 4.4.2 the authors demonstrate that their model exhibits similar performance across pocket finding algorithms, although we see a slight decline in some cases. I would like to see if training a model on pockets randomly sampled from multiple different approaches could be beneficial and allow the model to better generalize.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents AANet, a contrastive learning-based virtual screening framework designed to handle structural uncertainty in protein structures, particularly apo or AlphaFold2-predicted conformations where pocket annotations are absent or unreliable. AANet consists of two main components: (1) tri-modal alignment between ligands, holo pockets, and geometry-derived cavities; and (2) cross-attention-based aggregation to dynamically infer binding pockets from candidate cavities. The method is evaluated on curated DUD-E and LIT-PCBA subsets in both annotated and blind apo settings. It significantly outperforms strong baselines such as DrugCLIP, TankBind, and Glide, and demonstrates robustness across different pocket detection tools.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n1. The integration of ligand, holo pocket, and cavity representations enables improved robustness to pocket uncertainty.\n2. Cross-attention-based cavity aggregation allows training on pocket-agnostic data, expanding applicability to real-world bioactivity datasets.\n3. AANet achieves the best performance across the subset of DUD-E and LIT-PCBA.\n\n**Weaknesses**\n1. The paper does not provide information about the content and preprocessing steps of key datasets, such as ChEMBL35, used during training. This limits the transparency of the training process.\n2. In Appendix Table S8, AANet underperforms DrugCLIP on the full LIT-PCBA dataset, despite showing strong results on curated subsets. This raises concerns about its generalization ability and suggests that the model may rely on cavity augmentation.\n\n*Typo*\nLine 285: “holoand”\n\nQUESTIONS:\n1. Is there any discussion of ligand-based virtual screening methods (LBVS)? For example, traditional LBVS methods [1] and recent deep learning-based approaches [2] can bypass the need for protein structural information entirely.\n\n2. Is removing overlapping UniProt IDs sufficient to prevent information leakage between training and test sets? Given that homologous proteins with very high sequence similarity (e.g., >90%) may still exist in training sets, how do the authors ensure that models are not indirectly exposed to structurally or functionally similar targets?\n\n[1] Liu X, Jiang H, Li H. SHAFTS: a hybrid approach for 3D molecular similarity calculation\n \n[2] Zhou G, Wang Z, Yu F, et al. S-MolSearch: 3D Semi-supervised Contrastive Learning for Bioactive Molecule Search\n\nLIMITATIONS:\n1. The model relies on external cavity detection (e.g., Fpocket). If the cavity predictions are poor—especially in complex or flexible proteins—the model’s performance may degrade.\n2. AANet does not estimate confidence or uncertainty in its predictions, even under structurally noisy or blind settings, which can hinder decision-making in practical pipelines.\n3. The data split removes overlapping UniProt IDs but does not filter by sequence similarity. This may allow high-similarity homologs to leak between train and test sets, leading to inflated performance estimates.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces AANet, a novel virtual screening framework designed for structure-based drug discovery under structural uncertainty, particularly when working with apo or predicted protein structures lacking experimentally resolved binding pockets. AANet combines two key components: (1) a tri-modal contrastive alignment module that learns to align ligands, holo pockets, and geometry-detected cavities, and (2) a cross-attention-based aggregation module that dynamically scores and selects binding-relevant pockets from multiple candidates. The model is shown to significantly outperform both docking-based (Glide) and modern deep learning baselines (DrugCLIP, TankBind) in challenging blind apo settings, achieving near-holo performance. The authors also curate a benchmark derived from DUD-E and LIT-PCBA to evaluate under various pocket and structure scenarios.\n\nSTRENGTHS AND WEAKNESSES:\n[Strengths]\n\n* The combination of tri-modal contrastive alignment (between ligand, holo pocket, and detected cavity) and cross-attention aggregation is new and hasn't been used in previous methods.\n\n* The method shows large improvements compared to both traditional docking tools and recent deep learning approaches—especially in hard cases like blind apo structures.\n\n* The paper includes careful ablation studies and tests with different cavity detection tools, showing that the model is robust and generalizes well.\n\n[Weaknesses]\n\n* Even though the results are strong on DUD-E and LIT-PCBA, the method is not tested on more diverse or unbiased datasets, such as DEKOIS, or in real-world drug discovery tasks.\n\n* The model relies on external tools like Fpocket to find candidate pockets, which prevents end-to-end learning and may cause biases depending on the tool used.\n\n* The paper does not compare to some of the latest state-of-the-art methods, such as FlowDock or ProFSA, which also aim to handle apo structures under uncertainty. Notably, FlowDock includes an end-to-end pocket detection capability, making it more convenient to use. This highlights the need for a direct comparison between the two methods.\n\nQUESTIONS:\nCan the authors clarify whether any target-level or structure-level overlap exists between training and test sets?\n\nLIMITATIONS:\nNone", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper tackles the challenge of virtual screening on apo or predicted protein structures, where binding pocket locations are unknown. The authors propose AANet, a two-phase framework to address this structural uncertainty. First, a tri-modal contrastive learning scheme aligns representations of ligands, holo pockets, and detected cavities to learn robust features. Second, a cross-attention adapter aggregates information from multiple candidate cavities to infer the most likely binding site. Through extensive experiments, the authors demonstrate that AANet significantly outperforms state-of-the-art methods in this challenging blind screening setting, achieving performance comparable to that on ideal, ligand-bound structures.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. Addresses a timely and critical problem: The paper focuses on how to effectively utilize apo structures from tools like AlphaFold2 for virtual screening, a key bottleneck in current structure-based drug discovery. This work provides a viable solution for leveraging vast amounts of AI-predicted structural data, making it highly significant and impactful in a real-world context.\n2.  Elegant Methodological Design: The AANet framework, particularly its tri-modal contrastive alignment and ligand-conditioned cross-attention aggregation mechanisms, presents a highly original and well-tailored design to address the problem of pocket uncertainty.\n3. Thorough and Rigorous Experiments: The experimental design is highly rigorous. Notably, the authors constructed a benchmark with matched holo, experimental apo, and predicted apo structures, and designed multiple settings ('Oracle,' 'Annotated,' and 'Blind') to clearly dissect the sources of the method's advantages.\n\nWeaknesses:\n1. Lack of discussion on computational efficiency: The paper's core strength lies in its excellent performance, but it does not provide a quantitative analysis of its computational cost. A comparison of runtime between AANet and baselines like DrugCLIP would offer a more complete picture of the accuracy-efficiency trade-off, which is important for assessing its practicality in large-scale screening.\n2. The justification for some methodological parameters could be clarified: The authors mention increasing the pocket extraction radius from 6 Å to 10 Å to improve performance. While the ablation study demonstrates the effectiveness of this change, the choice of 10 Å appears somewhat empirical. A more detailed explanation of the selection criteria for such a key parameter would help readers better understand the method.\n\nQUESTIONS:\n1. The tri-modal contrastive learning is central to the proposed method. In this framework, the model is required to align with both the ideal 'holo pocket' and the 'detected cavity'. My question is: given that the ultimate goal is to predict on apo structures, what is the key role of aligning with the 'holo pocket'? Is this step primarily intended to provide a stable learning anchor to prevent the model from deviating on noisy data, or are there other, deeper considerations? This would help readers understand the intrinsic logic of the framework's design.\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-05"}