Title: Enhancing Bioactivity Prediction via Spatial Emptiness Representation of Protein-ligand Complex and Union of Multiple Pockets
Venue: NeurIPS 2025
Accepted: yes
Keywords: bioactivity prediction

drug discovery

target-ligand interaction
Abstract: Predicting the bioactivity of candidate ligands remains a central challenge in drug discovery. Ligands and endogenous substrates often compete for the same binding sites on target proteins, and the extent to which a ligand can modulate protein function depends not only on its binding but also on how effectively it occupies the relevant pocket. However, most existing methods focus narrowly on local interactions within protein–ligand complexes and neglect spatial emptiness—the unoccupied regions within the binding site that may permit endogenous molecules to engage or interfere. Such unfilled space can diminish the ligand’s functional impact, regardless of binding affinity. To overcome this key limitation in protein–ligand modeling, we propose LigoSpace, a novel method integrating three core components. LigoSpace introduces GeoREC (Geometric Representation of Spatial Emptiness in Complexes) to quantify atomic-level empty space and Union-Pocket to unify multiple protein pockets, providing a global view of binding sites. Additionally, LigoSpace employs a pairwise loss instead of commonly used MSE loss, to better capture relative relationships critical for drug discovery. Extensive experiments on multiple datasets with diverse bioactivity types demonstrate that LigoSpace significantly improves performance when integrated into state-of-the-art models, highlighting the effectiveness of its novel components.


Peer reviews:
comments: SUMMARY:
This paper proposes a method called GeoREC to address the bioactivity prediction problem. GeoREC quantifies the spatial emptiness around each atom within a protein-ligand complex, and introduces two key innovations: (1) the Union-Pocket, which aggregates multiple binding pockets to capture global interaction context, and (2) a pairwise loss function, which preserves relative ordering among predictions. Together, these enhancements improve the accuracy and selectivity of affinity prediction.

STRENGTHS AND WEAKNESSES:
Stengths: 
1. The proposed augmentation strategy consistently improves performance across various baselines, including DTIGN, GIGN, and GAT.
2. The pair-wise loss complements the regression loss by enhancing the selectivity for specific ligands and mitigating the effects of noise in affinity values.

Weaknesses:
1. What impact do different aggregation strategies have on the final performance?
2. Why does this method perform well on the SIU dataset, which contains complexes with a single binding pocket and a single ligand?
3. How does the method perform on the LBA (Ligand Binding Affinity) task?
4. If a ligand can bind to multiple positions in a protein with different conformations, how can multiple complex conformations be modeled? Does the method support using multiple complex conformations as input?

QUESTIONS:
See Weakness

LIMITATIONS:
This prediction method relies on the complex conformation structure. If the docking method generates inaccurate complex conformations, it may negatively affect the performance. Moreover, the method lacks analysis and discussion regarding this limitation.
TITLE: Official Review
CLARITY: 2
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-06-20

comments: SUMMARY:
This paper improves the bioactivity prediction task by considering spatial emptiness, union pockets and pairwise loss. The spatial emptiness aims to identify pockets and ligands atoms without sufficient contact. Union pockets combine pockets of different ligands to generate a more comprehensive view. Pairwise loss provides more precise supervision signal for bioactivity prediction. With these techniques, the authors have achieved substantial improvements on multiple benchmarks.

STRENGTHS AND WEAKNESSES:
Strengths:

1.	Pairwise loss and union pockets have incorporated important inductive bias into the bioactivity prediction task.
2.	The paper has achieved substantial improvement on multiple benchmarks and has conducted solid ablation tests.

Weaknesses:

1.	The authors did not compare their method with some strong baselines, for example, EHIGN that published on TPAMI.
2.	More experiments or explanation should be provided by claiming that GeoREC can model spatial emptiness.

QUESTIONS:
1.	Add more baseline methods:
There are a lot of strong baseline models for bioactivity prediction, and it’s better to make a more comprehensive comparison. For example, physics-based methods like MM-PB/GBSA, ABFEP or deep learning methods like EHIGN. 
2.	The implementation of GeoREC:
For neighbors in each cone, are they distinguished by their directions? Is this direction information incorporated into Egeo? If not, what is the difference between GeoREC and neighborhood-based edge construction?
3.	Why can GeoREC model spatial emptiness but other methods cannot?

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 2
IMPACT: 2
ORIGINALITY: 3
DATE: 2025-06-26

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a method to improve bioactivity prediction by combining three components: a novel geometric representation (GeoREC) designed to model the \"spatial emptiness\" within a binding site; a unified \"Union-Pocket\" that provides a consistent global context for the protein; and a pairwise-enhanced loss function to better preserve the relative ranking of bioactivities. Experiments are performed on \nDTIGN and SIU dataset to valid performance.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n1. The paper is generally well-written and clearly structured.\n\n2. The central motivation of modeling \"spatial emptiness\" is conceptually interesting.\n\n**Weaknesses**\n1. The claim of novelty for the pairwise loss function is overstated. Ranking-based or pairwise losses are well-established in computational chemistry and drug discovery, particularly for binding affinity prediction and virtual screening tasks. [1][2]\n\n2. The connection between the proposed GeoREC method and the concept of \"spatial emptiness\" is not sufficiently justified. The paper argues that GeoREC captures emptiness by connecting a central atom to the nearest neighboring atoms within predefined spatial cones. However, it is not immediately clear how these new edges, which connect existing atoms, directly represent or quantify the volume of \"unoccupied space\". A more rigorous explanation, perhaps with visualizations or a stronger biophysical argument, is needed to convince the reader that this geometric construction is a valid and effective proxy for spatial emptiness.\n\n3.  The experimental validation raises concerns about the generalizability and practical utility of the method. The primary DTIGN dataset consists of only 8 protein targets. This is a very small number and may not be representative of the vast chemical and structural space of protein targets. \n\n4. The set of baseline models is somewhat limited. While including DTIGN and GIGN is appropriate, several more recent and powerful state-of-the-art geometric deep learning models for protein-ligand interaction are missing, such SIGN[3], GET[4], MBP[2], and so on.\n\n[1]. A bioactivity foundation model using pairwise meta-learning, Nature Machine Intelligence,  2024\n\n[2]. Multi-task bioassay pre-training for protein-ligand binding affinity prediction, Briefings in Bioinformatics, 2024\n\n[3]. Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity, KDD, 2021\n\n[4]. Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning, ICML, 2024\n\nQUESTIONS:\n**Suggestions/Questions**:\n1. The central motivation of the paper rests on the importance of \"spatial emptiness\" for predicting bioactivity. However, the intuition behind this needs further development.  Could you please provide a more detailed explanation?\n2. Adding more powerful baselines, such as SIGN, GET, MBP, and so on.\n3. What is the main difference of the pairwise losses between GeoREC and previous work listed above?\n4. It's better to detail the advantages of predicting affinity based on docked conformations rather than co-crystal structures.\n\nI'm willing to raise my socre is these suggestions/questions are solved.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a new approach to structure-based bioactivity prediction by incorporating spatial information beyond traditional binding interactions. The authors introduce GeoREC, a method that models the unoccupied space around atoms in a protein-ligand complex, and Union-Pocket, which combines multiple pockets from the same protein to provide a consistent structural context across ligands. A hybrid loss function is also used to improve the preservation of bioactivity rankings. The method shows improved performance over some baselines on the DTIGN and SIU datasets, and an ablation study confirms that each component contributes to the overall gain.\n\nSTRENGTHS AND WEAKNESSES:\n*Strength*\n- The model introduces three well-defined components—GeoREC, Union-Pocket, and hybrid loss—and each is shown through ablation to provide a consistent improvement.\n- The paper is generally well-structured and clearly presented.\n- This research focuses on a core issue in the field of computational drug discovery: accurately predicting bioactivity based on protein-ligand complex structures.\n\n*Weakness*\n1. While the method performs well on certain SIU subsets, the results for Ki 0.9, Ki 0.6, and Kd 0.6 in the appendix are clearly below state-of-the-art levels. This raises concerns about the model's generalizability across different label types and dataset settings.\n2. The paper includes only a limited set of baselines. The lack of comparison with more diverse or competitive methods makes it difficult to judge how strong the proposed model is in a broader context.\n3. The abstract implies that stronger binding directly translates to stronger bioactivity. This is scientifically inaccurate. While binding affinity is a necessary component of bioactivity, it is not sufficient—many ligands bind tightly without eliciting a biological response, or even elicit off-target effects.\n4. The figures, particularly Figure 1, are not visually effective. Fonts are too small, and the graphical elements are not intuitive. Given that the method is fundamentally geometric, better visualizations are essential.\n5. There are some writing and LaTeX formatting issues. For example, terms like IC₅₀ and EC₅₀ are incorrectly written in italics or math mode. Similarly, the Pearson correlation coefficient should be denoted as lowercase r, not uppercase R.\n\nQUESTIONS:\n1. Union-Pocket appears effective on datasets like DTIGN and SIU, where multiple pockets per protein are available. However, how does the method apply to standard benchmarks like PDBbind [1], where proteins typically have only one known binding site? Can the authors comment on the generalizability of their approach in such single-pocket settings?\n2. What specific criteria were used to define individual pockets before forming Union-Pocket? The effectiveness of Union-Pocket depends on how the individual pockets are initially defined. Could the authors clarify how pockets are constructed during data preprocessing—for example, what criteria or radius are used? Since these pocket definitions may rely on heuristic rules or docking-based priors, to what extent does the Union-Pocket strategy introduce new information, rather than simply aggregating existing sampling bias?\n3.  While the three proposed components—GeoREC, Union-Pocket, and hybrid loss—are each shown to improve performance, the conceptual motivation for combining them is not clearly articulated. What is the biological or modeling rationale for combining GeoREC, Union-Pocket, and the pairwise loss in a single framework? Were they motivated together, or added independently for empirical gain?\n\n[1] Wang, Renxiao, et al. \"The PDBbind database: Collection of binding affinities for protein− ligand complexes with known three-dimensional structures.\" Journal of medicinal chemistry 47.12 (2004): 2977-2980.\n\nLIMITATIONS:\nYes. The authors acknowledge certain limitations in Appendix A.9, specifically the potential increase in computational cost due to the use of Union-Pocket.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}