AANet: Virtual Screening under Structural Uncertainty via Alignment and Aggregation
Authors: Wenyu Zhu
Jianhui Wang
Bowen Gao
Yinjun Jia
Haichuan Tan
Ya-Qin Zhang
Wei-Ying Ma
Yanyan Lan
Venue: NeurIPS 2025 poster ()
Keywords: Virtual screening
Drug discovery
Primary area: machine_learning_for_sciences

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).

AANet: Virtual Screening under Structural Uncertainty via Alignment and Aggregation
Wenyu Zhu Jianhui Wangl.S. Bowen Gao Yinjun Jia', Haichuan Tan Ya-Qin Zhang', Wei- Ying Ma', Yanyan Lan 12,31
'Inttitute for Industry Research, Tsinghua University, Beijing, China
Beijing Frontier Research Center for Biological Structure, Tsinghua University, Beijing, China BBeijing Academy Artificial Intelligence (BAAD, Beijing, China
*Deaarmeent of Computer Science and Technology. Tsinghua University, Beijing, China University of Electronic Science and Technology of China, Chengdu, China
Abstract
Virtual screening (VS) is critical component of modern drug discovery, yet most existing methods-whetthr physics-based or deep learning-based-aa 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 often missing. In this paper, we introduce
framework to enable accurate virtual screening under structural uncertainty. Our method comprises two core components: (1) tri-modal contrastive learning module that aligns representations of the ligand, the holo pocket, and cavities detected from structures, thereby enhancing robustness pocket localization error; and (2) a cross-altention 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 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 1175 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 wwt./cco///.//eeeeeeeeeeeeeeeoee Http//WleyyZAAAl AANet
Introduction
Virtual screening (VS) is a cornerstone of modern drug discovery, enabling fast and cost-cffective identification of potential small-molecule binders from large chemical libraries. Among various strate- gies, structure-based virtual screening (SBVS) is particularly prominent, using either physics-based docking [ 2 deep learning methods [3] to evaluate compound compound-pocket compatibility, typically experimentally resolved holo structures. However, most well-characterized holo targets have already been explored, limiting discovery opportunities. Recent advances in protein structure prediction, notably AlphaFold2 [4], have dramatically expanded structural coverage, enabling SBVS to target previously inaccessible proteins and supporting early-stage discovery of first-in-class therapeutics.
Eqqua contirbution
Coorespnnnence to lanyanyan@air. tsinghua. edu. en
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

apo-sred (oracks)
(onadi))
apo (annotaned)
holo
apo-cop: (Blind)
40
Entichment (1%)
30
20
10
apo (oradee, annotated)
8po (blind)
Glide (Docking)
DrugCLIP (Deep learning)
AANet (Ours)
Figure Performance comparison under holo and apo settings. The bar for docking in the apo (blind) setting is absent due to the high computational cost.
However, adapting existing SBVS methods to predicted structures remains significant challenge. Prior studies [5] structures. To address this, flexible refinement [9] and flexible docking techniques [TO] T1 [3]
have shown that docking performance degrades substantially in predict
have been proposed to adjust local pocket geometries and better accommodate ligands. However, these methods typically rely on pocket annotations derived from holo structure-an assumption that does not hold in realistic apo or predicted settings. Consequently, these methods largely overlook the upstream challenge of binding site identification under structural uncertainty- critical bottleneck that severely limits docking performance.
To systematically investigate this problem, curated benchmark derived from DUD-E [14] and LIT-PCBA [15., where holo protein structures are replaced by apo structures, including both predicted and experimentally determined conformations. Candidate pockets were identified using the widely adopted detection tool Fpocket [16]. Our comprehensive evaluation on this benchmark reveals that while deep learning methods such as DrugCLIP (3] exhibit greater robustness local conformational variation than traditional docking approaches, they still suffer significant performance drops when applied fully predicted apo structures. These findings indicate that effectively modeling and learning the discrepancies between pockets predicted by detection software and the actual ligand- binding sites crucial challenge for successful virtual screening apo settings.
To address this challenge, we propose AANet, an Alignment- diinmeent-and-peeee Aggreegttt framework to improve virtual screening under structural uncertainty in Apo and AlphaFold (predicted) structures. The first component, alignment, implemented as a tri-modal contrastive learning scheme, leveraging the insight that pocket detection tools identify geometric cavities, while holo-structure represent actual ligand- bandddg regions. Our method takes three inputs-the ligand, the holo pocket, and the detected cavity-aun and learns pairwise alignments through contrastive objectives. This alignment encourages the model to learn robust and transferable representations across structural discrepancies. To enhance this process, we incorporate a hard negative sampling strategy among candidate cavities, forcing the model to distinguish true binding sites from geometrically plausible but functionally-irreeevy pockets. Building upon this pocket-aware aligmment, the second component, aggregation, employs cross-attention adapter module for dynamic integration of information across multiple candidate cavities. This allows the model to softly weigh pocket representations, infer binding-relevant regions and effectively leverage pocket-agnostic activity data.
Our framework outperforms both paysics-based and DL-based baselines, achieving near-holo perfor- mance on both predicted and experimental apo structures. This improvement is supported by strong pocket identification accuracy and robustness across different pocket detection algorithms, suggesting that the model captures spatial features intrinsic to the structure rather than overfitting to a specific detector. These results highlight the potential of our approach to enable SBVS in more realistic and structurally uncertain drug discovery scenarios, especially where holo structures are unavailable.
In summary, contributions are follows:
(1) Revealing the bottlenecks of SBVS under structural uncertainty. We formalize the problem of virtual screening without reliable pocket definitions, and introduce a benchmark based on DUD-E

---

and LIT-PCBA for systematic evaluation on predicted and experimental apo structures. Our analysis shows that degradation mainly arises from pocket mislocalization, rather than structural noise.
(2) Alignment and aggregation for uncertain-pocker SBVS. We propose a tri-modal contrastive learning framework that aligns ligands with geometry-derivee cavities via cavity-based augmentation and hard negative mining. A cross-attention adapter further aggregates signals across candidate pockets, enabling training on pocket-agnostic data.
(3) Enabling virtual screening beyond holo structures. Our method achieves performance under structural uncertainty comparable that on holo structures, enabling screening targets without annotated binding sites and expanding the reach structure-bast based drug discovery.
Related work
Traditional docking methods such as AutoDock [2] and Glide [] rely on physics-based scoring
functions to evaluate target-ligand interactions. range DL-based approaches have emerged that learn scoring functions from protein- protein-ligand poses [ 18
21 22 or predict interactions adopt a novel contrastive paradigm inspired by CLIP, aligning ligands and protein pockets in shared embedding space, thus representing a new direction in SBVS.
19
20,
based on structural inputs [23 24 Recent methods such as DrugCII
Nonetheless, most of these methods assume access to high-quality holo structures and overlook the practical challenges posed by predict apo proteins. The accuracy of conventional docking deteriorates significantly in these settings due to the lack of ligand-induced conformational changes 6 7 8] although some approaches attempt to mitigate this through flexible modeling [ or by leveraging homologous holo structures [5]. However, DrugCLIP and other DL-based methods exhibits robustness structural perturbations; Instead, our analysis suggests that is highly sensitive to the location and quality of the predefined binding pocket in holo structures.
3 Method
3.1 Formulating virtual screening under structural uncertainty
SBVS aims to identify bioactive molecules from a candidate library .M- = {m1. m2...., mn,h protein structure 2n, R In conventional settings, the protein is provided in holo form, and the binding pocket P; is defined by the spatial neighborhood surrounding a co-crystallized ligand
} given
Tm R
Pt Pt= Fn E TR33
min me().....M)
|| Im <
where d typically set to 6 Å. This holo pocket allows physics-based docking methods or learning- based scoring functions to estimate binding affinity.
However, holo structures are often unavailable in realistic drug discovery pipelines. Instead, predicted or experimental apo structures are used, in which the protein has not undergone ligand-induced conformational changes. In such cases, where ligand positions are unknown, binding pockets must be inferred solely from the protein structure. To address this, various pocket detection tools have been developed [16 25 26 27 28). They identify potential binding sites by characterizing geometric cavities on the protein surface. Specifically, let {z { 1a-1 be the centers of S cavities detected by pocket prediction software. candidate pocket is then defined as:
p(e) = In E 3 ||
{1) S <d
Given an apo protein structure xn and of candidate pockets p= identified via geometric cavity detection, the problem of structure-based virtual screening (SBVS) under structural uncertainty is to accurately identify bioactive molecules from the compound library M.
3.2 Disentangling failure modes in virtual screening without holo structures
While prior studies 6 have reported significant performance drops when using apo or predicted structures, their focus has largely been traditional docking methods. In this work, we systematically
3

---

10
Expand Radius
Ligand defined
Hola pocket shunt
loU
Detection
Complex
Pocket & Cavities
Figure 2: Cavity-based based pocket augmentation with hard negative mining. For each pootein-ligane complex, the holo pocket is defined by the ligand, and a pocket detection tool scans cavities on the protein structure. Cavities are labeled positive or negative based on their IoU with the holo pocket.
evaluate both locking-based and deep learning (DL.) approaches, identifying the key challenges that limit performance under structural uncertainty.
We curated a benchmark from the DUD-E dataset 14] to assess SBVS under both structural and pocket-level uncertainty. For each target, we collected matched of experimentally determined holo and apo structures, well as across structure types under consistent grgand-targes pairs. A total of 38 targets with all three structure types were retained. Compared to holo structures, apo and predicted proteins lack ligand-inde induced conformational changes, introducing two types of uncertainty: (i) structure mismatch, where the backbone conformation differs but pocket location is assumed fixed; and (ii) pocket localization mismatch, where the true binding site must be inferred due to missing ligand supervision. To isolate these effects, we define evaluation settings along two axes. From the structure perspective, we consider apo-exp (experimental apo) and apo-pred (AlphaFold2). For pocket localization, we evaluate: oracle, where the ligand-defined pocket is used; annotated, where the detected cavity with highest IoU to the true pocket is selected; and blind, where no ligand or annotation available and pockets are detected purely from geometry.
models (9. enabling controlled comparisons
We evaluated Glide and the DL-based DrugCLIP (3] across these settings. As shown in Figure ! and Table Glide shows a sharp drop even in the apo-exp (oracle) setting, revealing its sensitivity contrast, DrugCIIIP maintains performance across holo, apo-exp,
to conformational changes. and apo-pred (oracle), indicating robustness to moderate structural noise. However, its performance declines significantly in the annotated and blind settings, where pocket localization uncertain. Even slight misalignments in pocket position lead to notable degradation (see Appendix (A.I).
These findings expose key limitation of current DL-based SBVS methods: while tolerant structural variation, they remain dependent on accurate pocket definitions. Without them, performance drops sharply- highlighing the need for models that can infer relevant pockets under uncertainty. In the next section, we introduce contrastive learning framework that addresses this challenge through cavity alignment and multi-pocket aggregation.
3.3 Tri-modal contrastive alignment
We propose a tri-modal contrastive alignment framework that aligns representations across ligand, holo pocket, and cavity modalities overcome localization-induced failures. The key idea is to disentangle pocket representations from their dependence on ligand -igfnn-defined positions and encourage alignment with -eometry-derived cavities.
Cavity-based pocket augmentation via proxy selection. Due to the performance loss caused by pocket deviation,
introduced pocket-side augmentation that aligns both holo ligand and holo pocket with cavity intrinsically residing on the protein structure. To be specified, we adapt Fpocket [16) to detect potential pockets, and randomly select one candidate cavity P. whose overlap with the holo pocket P/ exceeds a predefined threshold t, measured by Intersection over Union (IoU):
(P,, = P Pal
(3)
P.UP

---

g())
Ligand Encoder
G())
W Winear +
Ligand
Linear Projection
#/
43
Query Q = ((1) -Wo
Pocket Encoder
F(P)
Pr
Piz
Pyx
PrP1s
22P
Wx
K
Softmax
Pocket
OF
Share Weights
Wy
V
a.Vi
Pst
P32
SyP33
22P32
8755
Cavity Encoder
F (P)
1C1
Cross-aftention Layer
Cavity
Adapter
Training
Fine-tuning
Ligand
Ligand Encoder
Ligand
Ligand Encoder
Linear
PDBaind
Pocket
Pocket Encoder
LeL
Complex
CHEMBL
Activity
KL
Maspee
C'
a1
Cavity
Cavity Encoder
Candidates
Pocket
( (AF2 struct
Figure 3: Model framework. AANet operates in two phases: alignment and aggregation. During alignment, representations of the ligand, holo pocket, and davity-encodee separately- are aligned via contrastive losses. In the aggregation phase, the encoders are frozen, and a cross-attention module aggregates representations from candidate cavities (via the cavity encoder) using the ligand embedding as the query. This phase is trained on annotations. The ligand embedding further projected through trainable linear layer, and a final contrastive loss aligns the adapted ligand and aggregated cavity representations.
structures without pocket
Contrastive objective. The detected cavity is treated third modality alongside the holo pocket Pt and the ligand I. Let J and G denote the encoders for holo pockets and ligands, respectively, and let F, (sharing weights with FF) encode detected cavities. We define two positive pairwise-sige sigmood loss (29 functions: one for pocket-ligand pairs (P, I), and one for pocket-pocket pairs (Pc, Pt):
(p,(P,t) log(1 (1 + e -(( Cppp(Pe, PI) log + ( (4)
where "." the dot product, 0 learnable temperature, and is learnable bias. Combining these, the positive alignment objective becomes:
SCL = Cp. G(D) + C.. (F.(P). G(I) (F.PP)
(5)
Aligning both the holo pocket and the holo ligand with the detected pocket modality mitigates overfitting caused by ligand-dependent pocket extraction. This encourages the model to learn intrinsic spatial features of the protein structure rather than artifacts closely tied the holo ligand's location.
In addition, given the observation that the detected pockets can substantially deviate from holo pockets, especially when cramped regions within pocket cause the detected cavity to split into smaller sub sub-pockets (see Appendix A.2). we increased the pocket extraction radius from 6 Å to 10 Á while maintaining the same maximum number of atoms by applying atom nown-sampling to mitigate this issue.
Hard negative mining from non-binding cavities. We then complement the positive terms with negative sampling to equip the model with the ability to distinguish true binding pockets from a set of candidate cavities. We introduce negative samples by selecting non-binding detected pockets during training. Specifically, half of the detected cavities with low loU are treated as non-binding P- and used as negative pairs under the same pairwise-sigma loss (Equation with label -1 for both (P=, and (P= P.).
5

---

3.4 Cross-attention Adapter for Cavity Aggregation
Given that AANet has learned to distinguish binding from non-binding cavities via contrastive supervision, we extend its upplicability to activity datasets where the true binding site is unknown. To enable dynamic inference over multiple candidate cavities, we introduce a lightweight cross-attention adapter that aggregates cavity embeddings conditioned on the ligand representation.
Cross-attention design. The adapter consists of single-head dot-product attention layer the cavity side and linear projection on the ligand side. Given ligand embedding G()) and cavity embeddings (Fs(P( the adapter computes unified cavity representation:
é
a FF(!!!))
(6)
where attention weights (+)) are computed with the ligand as query and cavity embeddings as keys and values. The ligand is projected to é and used in contrastive alignment with é
Initialization. We initialize the adapter near identity: cavity embeddings are initially averaged, and the ligand embedding remains unchanged. The attention temperature is set high so that softmax approximates a hard max, smoothly transitioning from ensemble scoring to learnable attention during fine-tuning.
Training with pocket-agnostic data. To enable training without known binding annotations, adopt two stabilization strategies: (1) retain a subset complex complex-based samples with known binding pockets; and (2) supervise attention weights using soft or hard labels depending the data source. For activity-only samples, we use the pretrained AANet's 's cavity scores as soft labels. For complex- based samples, apply one-hot labels based ground-truth high-IoU cavities. Attention supervision is implemented via KL divergence:
Ckk =
Σ
st log
aj
where is either a soft distribution or a one-hot vector.
We also define a contrastive loss between the aggregated cavity embedding é, and projected ligand embedding ét, using the pairwise-sigmoid form from Equation
fea = log (1 + -
(8)
where and b are shared with the pretraining objective. The final training objective combines both terms:
Lagg f'a + X L'LL:
allowing the model to infer kinding-relevant cavities through dynamic cross-attention, even in the absence of explicit structural annotations.
4 Experiments
4.1 Experimental settings
Training. Our model was initialized with Uni-Mol 30 and fine-tuned on the PDBBind 2020 general set [31], with all entities overlapping with DUD-E or LIT-PCBA removed. For dynamic aggregation training, binding and activity data from ChEMBL33 (32 (pre-processing described in B.2 were filtered and mapped to predicted apo structures from the AlphaFold Protein Structure Database 33 All UniProt entries matching any PDB entity in DUD-E or LIT-PCBA were excluded to avoid data leakage. The model directly predicts binding scores from protein-ligamt pairs and is evaluated in virtual screening setting without any task-specinf fine-tuning.
Evaluation dataset. We evaluated our model on two virtual screening datasets: DUD-E 14 and LIT-PCBA 15 The target list was curated to ensure fair comparison across structural sources under controlled conditions. For DUD-F, we used a subset of 38 targets for which all three structure types available ciiilall-expprimmettall resolved apo structures, AlphaFold2-predictee structures, and

---

Notably, its holo structures from the original DUD-E E dataset. For LIT-PCBA, experimental apo structures were and predicted apo structures were obtained 33 Twelve targets were retained, and three were excluded: VDR and OPRK1 due to the absence of experimental apo structures, and mTTOR1 because all holo ligands bind at heterogeneous protein-protee
manually selected with the assistance of AHoJ 34
from the AlphaFold Protein Structure Database
interface, which is not represented in the AlphaFold Protein Structure Database. The evaluation settings follow those described in Section 3.2 The COACH420 dataset for pocket identification was obtained from the P2Rank (28 repository and deduplicated against PDBbind.
Metrics. To assess screening performance, we adopted standard virtual screening metrics. AUROC (Area Under the Receiver Operating Characteristic Curve) measures overall ranking performance across all thresholds. EFI% (Enrichment Factor at 1%) measures the fold increase in the proportion of actives within the top 1% of ranked compounds compared the full dataset, reflecting early recognition ability. BEDROC (with a 80.5) is weighted variant of the AUROC that emphasizes early enrichment while accounting for overall ranking quality. All metrics were computed per target and averaged across tasks assess model robustness.
Baselines. For docking- -based baselines, we included Glide 1 (Standard Precision) and rescoring method RTMScore [22] and EquiScore [21]. Only the top-1 pose per ligand from Glide was retained for fair comparison. We also included TankBind 23 and DrugCLIP as docking-free baselines.
4.2 Screening from holo to apo settings
Table 1:
Performance on DUD-E and LIT-PCBA. Each method evaluated on three structural subsets: holo, apo (experimental), and apo (predicted), under both annotated and blind settings. Bold numbers indicate the best performance each dataset-subset configuration. Row colors indicate
method type:
Docking Rescoring. Docking- bascline and Proposed method
BEDROC 50.5)
EF1%
Method
holo
upo-exp (annot)
ape-pred (amnot)
apo-cxp (blind)
apo-pred (blind)
holn
apo-exp (annot)
apo-pred (annot)
ape-exp (blind)
apo-pred (blind)
DUD-E (n = 38)
Glide-SP
0.2958
0.1427
0.1761
17.25
7.74
9.16
RTMScore
0.4311
0.1918
26.34
11.47
11.74
EquiScore
0.2479
0.1466
0.1644
14.46
8.44
9.89
TankBind
0.2886
0,2996
0.3008
0.3074
0.2930
17.03
18.13
17.76
18.52
17.36
DrugCLIP
0.5157
0.3493
0.3746
0.1926
0.1974
33.70
21.36
22.70
11.75
12.05
AANet
0.6365
0.5866
0.6003
0.5764
0.6232
40.85
38.03
38.46
37.19
40.85
LIT-PCBA
12)
Glide-SP
0.0565
0.0503
0.0323
5.05
3.06
1.42
RTMScore
0.0445
0.0173
0.0205
3.34
0.67
1.16
EquiScore
0.0556
0.0200
0.0511
4.06
1.27
3.24
TankBind 0.0455
0.0491
0.0472
0.0496
0.0430
3.47
3.21
3.14
3.42
2.90
DrugCLIP
0.0690
0.0554
0.0483
0.0210
0.0155
5.96
4.51
2.85
1.54
0.88
AANet 0.0850
0.0730
0.0805
0.0630
0.0715
7.54
5.58
6.64
3.92
5.40
From holo to apo with annotated pocket.
On the both benchmarks, AANet achieves the highest scores in BEDROC and EF1 % across holo and both apo settings, and ranks best in AUROC as well (details in Appendix C.2) performance remains steady across structural conditions, demonstrating superior robustness to pocket uncertainty and backbone variation. In contrast, both -ocking- based methods (including rescoring) and the docking free DrugCLIP show substantial performance degradation. TankBind maintains consistent performance likely due to use of detected pockets during training rather than ligand- defined ones, well its large binding region radius (up to 20 A), which in some cases covers the entire protein when the target is small.
From annotated to blind.
To evaluate model performance fully apo scenario- where only apo structures are available and no reliable binding pocket annotations are assumed- we provide the model with all cavities detected from the structure and allow to infer the binding site autonomously. Since protein may contain multiple potential pockets, but each ligand typically binds to specific site, we define the final
7

---

score for each compound as the maximum score across all candidate pockets associated with that protein. As shown in T AANet maintains stable performance across metries, while DrugCLIP continues to decline. Due to the computational burden of the docking search phase under blind settings-Whetthr across multiple candidate pockets or via global search- docking and rescoring methods are not evaluated in this setting. It is unsurprising that TankBind maintains consistent performance, as this phase primarily relies on pocket identification, and its training set was not deduplicated against the targets in either benchmark. Our further analysis on pocket identification reveals that its generalization ability is significantly inferior to that of AANet.
4.3 Pocket identification with holo ligand
Table 2: Pocket identification performance on COACH420 dataset (n = 433 ligands/pockets on 288 structures) at various distance cutoff thresholds (Å) from pocket center to any ligand heavy atom (DCA). "Top-1" is the fraction of cases where the highest-scoring pocket lies within the given DCA; "Top-n" is the fraction where any of the top n. pockets lies within that DCA. The oracle row is shaded in gray; bold entries denote the best among non-oracle methods.
Top-1 (DCA S x AA
Top-n (DCA S x (z))
Method
2
3
1
2
3
4
Ideal (Oracle)
0.1488
0.5512
0.7070
0.7558
0,1488
0.5512
0,7070
0.7558
PRANK
0.1290
0.3690
0,4800
0,5440
TankBind
0,0728
0.2887
0.3850
0.4178
0.0798
0.3122
0.4155
0.4507
DrugCLIP
0.0535
0.2651
0.3372
0.3442
0.0605
0.2837
0.3605
0.3721
AANet (w/o agg)
0,1047
0,3837
0,5000
0.5256
0.1140
0.4116
0.5395
0,5698
AANet
0.1140
0.4140
0.5419
0.5721
0.1196
0.4349
0.5744
0.6047
Since AANet has demonstrated near-holo performance even under uncertainty in pocket structure and location, attribute this robustness to AANet's ability to effectively identify and distinguish true binding pockets. We further compare the pocket identification ability docking-free deep learning methods against the classical ligand-free PRANK method to decompose its power in blind settings.
To further validate this capability, we evaluate AANet on the COACH420 dataset 28 As shown in Table AANet significantly outperforms the baseline models TankBind and DrugCLIP across all distance thresholds, and consistently achieves the highest top-1 and top-n pocket identification accuracy among non-oracle methods. It is only marginally outperformed by PRANK in the top-n DCA at Å, a setting in which the oracle performance is notably low. These results confirm that AANet not only achieves strong screening performance but also excels at identifying the correct binding pocket among multiple candidates, underlining its robustness under blind apo settings.
4.4 Ablation and analysis
4.4.1 Ablation on difffrrnt modules
We conduct ablation studies on the DUD-E dataset (n 38) to evaluate the individual contributions of each module in our framework. For the alignment phase, we use standard contrastive pocket-molecule learning as the baseline and ablate three key components: cavity augmentation, negative sampling, and pocket enlargement. As shown in Table 3 each component contributes positively to performance, demonstrating its effectiveness.
In the aggregation phase, we assess the impact of adapter design by comparing our attention-base adapter with a simple argmax-based alternative. Results show that the attention-based adapter leads to substantial improvement, while the argmax version performs similarly to the model prior to aggre- gation highlighting the importance of learning adaptive attention weights. Finally, incorporating pocket label supervision during adaptation yields an additional performance gain.
4.4.2 Generalization different pocket detection methods.
Since AANet was primarily trained and evaluated using pockets detected by Fpocket, one potential concern is whether its performance stems from overfitting to Fpocket-specific biases rather than
8

---

Table 3: BEDROC scores on DUD-E 38) under different ablation settings. CA cavity
augmentation, NS negative sampling. EP = enlarged pocket; PA pocket adapter, PL = pocket
label supervision. For PA column, M argmax pocket adapter, cross-attention adapter.
Module
BEDROC
apo-exp (blind)
apo-pred (blind)
NS
PA
Alignment phase
contrastive pocket-molecube learning
0.0838
0.0822
cavity augmentation
0,2007
0.2341
negative sampling
V
0,4016
0,4097
enlarged pocket
V
0.4708
0,4585
Aggregation phase
w/ argmax adapter
M
x
0.4639
0.4620
w/ attention adapter
A
X
0.5550
0.5996
pocket label (AANet)
0.5764
0.6232
Table 4: Performance of AANet across different pocket detection methods and structural subsets. apo-pred-t denotes truncated predicted apo structures.
Detector
apo-exp
apo-pred
apo-pred-t
AUROC
BEDROC
EF1%
AUROC
BEDROC
AUROC
BEDROC
EF1%
Fpocket 0.7956
0.5147
32.95
0.8359
0,5367
34.54
0,8348
0.5589
36.06
PocketFinder
0.8061
0.5059
32.22
0.7620
0.4678
30.28
0.8161
0.5434
35.23
SURFNET 0.7906
0.4971
32.00
0.7572
0.4331
28.01
0.8274
0.5507
35.93
LIGSITE 0.7989
0.4838
30.97
0.7669
0,4532
29.13
0.8496
0.5816
38.10
learning features intrinsic to the protein structure. To address this, we evaluated AANet using alternative pocket detection methods (25) As shown in Table AANet maintains comparable level of performance on experimental apo structures among alternative pocket detection methods, while that predicted ones shows slight decline. To further investigate this discrepancy, re-detected pockets and re-evaluated performance on predicted apo manually truncated low-confidence regions near the binding site (denoted as apo-pred-t). AANet remained stable across detectors apo-pred- t, suggesting that the observed gap on full predicted apo structures is more likely attributable to differences in robustness of the detection methods to structural inaccuracies, rather than inherent limitations of the model itself. These results indicate that AANet generalizes well across different pocket sources and does overly rely on artifacts introduced by specific detector.
4.4.3 t-SNE analysis of embedding consistency under pocket uncertainty
holo ape spe-dred
holo apo exp apo pred
Figure 4: t-SNE comparison of pocket-ligand embeddings from three structures. (a) DrugCLIP: embeddings for holo and apo-exp/pred (annonated) pockets are widely separated. (b) AANet: embeddings for each target cluster closely.

---

We randomly selected 20 targets from DUD-E with holo and both apo pockets (annotated) whose IoU values fell within moderate range (0.3, For both DrugCLIP and AANet, pocket and holo ligand embeddings were combined via element- wise multiplication to form joint representations, which were projected to two dimensions using 1-SNE [35]. As shown in Figure DrugCLIP's embeddings for apo-exp and apo-pred detected pockets lie close to each other but remain distant from the corresponding holo embeddings, indicating a lack of robustness to pocket variation. contrast, AANet produces tightly clustered embeddings across holo, apo-exp, and apo-pred conditions for each target, demonstrating strong consistency and pocket-invariant representation.
Conclusion
We present a new benchmark and systematic evaluation of the challenges faced by DL-based SBVS methods in the apo setting, where binding pockets are unknown and structural conformations are often imprecise. To address these challenges, we propose AANet, novel framework composed of tri-modal alignment and dynamic pocket aggregation. AANet achieves near-holo performance even under blind apo conditions. By bridging the gap between Al-based VS and real-world drug discovery needs, our method enables more effective use of predicted structures (c.g., AlphaFold2), extending the applicability of structure-bases VS wider range of novel protein targets and first-in-class scenarios.
Acknowledgements
This work is supported by Beijing Academy of Artificial Intelligence and Beijing Frontier Research Center for Biological Structure Fundings.
References
[1] Richard A. Friesner, Jay L. Banks, Robert B. Murphy, Thomas A. Halgren, Jasna J. Klicic, Daniel T. Mainz, Matthew P. Repasky, Eric H. Knoll, Mee Shelley, Jason K. Perry, David Shaw, Perry Francis, and Peter S. Shenkin. Glide: A New Approach for Rapid, Accurate Docking and Scoring. Method and Assessment of Docking Accuracy. Journal of Medicinal Chemistry, 4777):1739-1749, March 2004.
[2] Oleg Trott and Arthur J. Olson. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. Journal of computational chemistry, 31(2):455-46) January 2010.
[3] Bowen Gao, Bo Qiang, Haichuan Tan, Yinjun Jia, Minsi Ren, Minsi Lu, Jingjing Liu, Wei- NeigYing Ma, and Yanyan Lan. Drugelip: Contrastive screening. Advances in Neural Information Processing Systems, 36:44595 44644495544444444114411444444444 2023.
molecule representation learning for virtual
[4] John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ron- neberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, and Demis Hassabis. Highly accurate protein structure predic- tion with AlphaFold. Nature, 596(7873):583- 589, August 2021. Publisher: Nature Publishing Group.
[5] Christian Kersten, Steven Clower, and Fabian Barthels. Hic Sunt Dracones: Molecular Docking in Uncharted Territories with Structures from AlphaFold2 and RoseTTAfold. Journal of Chemical Information and Modeling, March 2023. Publisher: American Chemical Society.
[6] Anna M. Dovira, Helena Martín, Thijs Beuming, Lucía Díaz, Victor Guallar, and Soumya Ray. Are Deep Learning Structural Models Sufficiently Accurate for Virtual Screening? Application of Docking Algorithms to AlphaFold2 Predicted Structures. Journal of Chemical Information and Modeling, March 2023. Publisher: American Chemical Society.
10

---

[7] Matthew Holcomb, Ya-Ting Chang, David S. Goodsell, and Stefano Forli. Evaluation of AlphaFold2 structures as docking targets. Protein Science, 32(1))e4530, 2023. _eprint:
[8] Shukai Gu, Yuwei Yang, Yihao Zhao, Jiayue Qiu, Xiaorui Wang, Henry Hoi Yee Tong, Liwei Liu. Xiaozhe Wan, Huanxiang Liu, Tingjun Hou, and Yu Kang. Evaluation of AlphaFold2 Structures for Hit Identification across Multiple Scenarios. Journal of Chemical Information and Modeling, April 2024. Publisher: American Chemical Society.
[9] Yuqi Zhang, Marton Vass, Da Shi, Esam Abualrous, Jennifer Chambers, Nikita Chopra, Christopher Higgs, Koushik Kasavajhala, Hubert Li, Prajwal Nandekar, et al. Benchmarking refined and unrefined alphafold2 structures for hit discovery. Journal of Chemical Information and Modeling, 63(6): 1666-16666167, 2023.
[10] Jintao Zhu, Zhonghui Gu, Jianfeng Pei, and Luhua Lai. DiftBindFR: an SE(3) equivariant network for flexible motein-ligant docking. Chemical Science, 15(21)7792--742, 2024. Publisher: Royal Society of Chemistry.
[11] Michael Plainer, Marcella Toth, Simon Dobers, Hannes Stark, Gabriele Corso, Céline Marquet, and Regina Barzilay. DiffDock-Pocket: Diffusion for Pocket-Level Docking with Sidechain Flexibility. In NeurlPS 2023 Workshop on New Frontiers of Al for Drug Discovery and Development (A14D3), December 2023.
[12] Yufei Huang, Odin Zhang, Lirong Wu, Cheng Tan, Haitao Lin, Zhangyang Gao, Siyuan Li, and Stan Z. Li. Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge, February 2024. arXiv:2402.11459 [physics, q-bio].
[13] Alex Morehead and Jianlin Cheng. Flowdock:
Geometric flow matching for generative protein- ligand docking and affinity prediction. In Intelligent Systems for Molecular Biology (ISMB), 2025.
[14] Michael M Mysinger, Michael Carchia, John Irwin, and Brian K Shoichet. Directory of useful decoys, enhanced (dud-e): better ligands and decoys for better benchmarking. Journal of medicinal chemistry, 55(14):6582-659, 2012.
[15] Viet-Khoa Tran-Nguyen, Célien Jacquemard, and Didier Rognan. LIT-PCBA: An Unbiased Data Set for Machine Learning and Virtual Screening. Journal of Chemical Information and Modeling, April 2020. Publisher: American Chemical Society.
[16] Vincent Le Guilloux, Peter Schmidtke, and Pierre Tuffery. Fpocket: open source platform for ligand pocket detection. BMC bioinformatics, 10:1-11, 2009.
[17] Jacob D. Durrant and J. Andrew McCammon. NNScore 2.0: Lir-nd Scoring Function. Journal of Chemical Information and Modeling, 51(11))2897- 2903, November 2011. Publisher: American Chemical Society.
Neural-Network Recep-
[18] David Ryan Koes, Matthew P. Baumgartner, and Carlos Camacho. Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise. Journal of Chemical Information and Modeling, 53(8):1893-1904 August 2013. Publisher: American Chemical Society.
[19] Andrew T. McNutt, Paul Francoeur, Rishal Aggarwal, Tomohide Masuda, Rocco Meli, Matthew Ragoza, Jocelyn Sunseri, and David Ryan Koes. GNINA 1.0: molecular docking with deep learning, Journal of Cheminformatics, 13(1), June 2021.
[20] Dejun Jiang, Chang- Yu Hsieh, Zhenxing Wu, Yu Kang, Jike Wang, Ercheng Wang, Ben Liao, Chao Shen, Lei Xu, Jian Wu, Dongsheng Cao, and Tingjun Hou. InteractionGraphNet. A Novel and Efficient Deep Graph Representation Learning Framework for Accurate Protein-Ligand Interaction Predictions. Journal of Medicinal Chemistry, 68024)):8209 2021. Publisher: American Chemical Society.

---

[21] Duanhua Cao, Geng Chen, Jiaxin Jiang, Jie Yu, Runze Zhang. Mingan Chen, Wei Zhang, Lifan Chen, Feisheng Zhong, Yingying Zhang, Chenghao Lu, Xutong Li, Xiaomin Luo, Sulin Zhang, and Mingyue 7.eng. Generic protein-ligand interaction scoring by integrating physical prior knowledge and data augmentation modelling. Nature Machine Intelligence, 6(6):688-700, June 2024. Publisher: Nature Publishing Group.
[22] Chao Shen, Xujun Zhang, Yafeng Deng, Junbo Gao, Dong Wang, Lei Xu, Peichen Pan, Tingjun Hou, and Yu Kang. Boosting Protein-Ligand Binding Pose Prediction and Virtual Screening Based on Residue-Atom Distance Likelihood Potential and Graph Transformer. Journal of Medicinal Chemistry, (5(15):069111107000 August 2022.
[23] Wei Lu, Qifeng Wu, Jixian Zhang, Jiahua Rao, Chengtao Li, and Shuangjia Zheng. TANKBind: Trigonometry-Awee Aware Neural NetworKs for Drug-Protein Binding Structure Prediction, Advances in Neural Information Processing Systems, 35:7236-7249, December 2022.
[24] Xujun Zhang. Odin Zhang, Chao Shen, Wanglin Qu, Shicheng Chen, Hanqun Cao, Yu Kang, Zhe Wang, Ercheng Wang, Jintu Zhang, Yafeng Deng, Furui Liu, Tianyue Wang. Hongyan Du, Langcheng Wang, Peichen Pan, Guangyong Chen, Chang- Yu Hsieh, and Tingjun Hou, Efficient and accurate large library ligand docking with KarmaDock. Nature Computational Science, 3(9)7799-804, September 2023. Publisher: Nature Publishing Group.
[25] John A. Capra, Roman A. Laskowski, Janet M. Thornton, Mona Singh, and Thomas A. Funkhouser. Predicting Protein Ligand Binding Sites by Combining Evolutionary Sequence Conservation and 3D Structure. PLoS Computational Biology, 2009.
December
[26] Marylens Hernandez, Dario Ghersi, and Roberto Sanchez. SITEHOUND-webh a server for ligand binding site identification in protein structures. Nucleic Acids Research, 37( Server issue): W413-W)611 July 2009.
[27] Andrea Volkamer, Daniel Kuhn, Friedrich Rippmann, and Matthias Rarey. DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment. Bioin- formatics, 28(15):0044-075. August 2012. Publisher: Oxford Academic.
(28) Radoslav Krivák and David Hoksza. P2rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure. Journal of cheminformatics, 10:1-12, 2018.
[29] Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid Loss for Language Image Pre-Training. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 11975-11986, 2023.
[30] Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, and Guolin Ke. Uni-mol: A universal 3d molecular representation learning framework. In The Eleventh International Conference on Learning Representations, 2023.
[31] Zhihai Liu, Minyi Su, Li Han, Jie Liu, Qifan Yang, Yan Li, and Renxiao Wang. Forging the basis for developing pigdein-ligand interaction scoring functions. Accounts of chemicul research, 50(2):302-309, 2017.
[32] David Mendez, Anna Gaulton, A. Patrícia Bento, Jon Chambers, Marleen De Veij, Eloy Félix, María Paula Magariños, Juan F. Mosquera, Prudence Mutowo, Michat Nowotka, María Gordillo-Marahón, Fiona Hunter, Laura Junco, Grace Mugumbate, Milagros Rodriguez-Lopez, Francis Atkinson, Nicolas Bosc, Chris J. Radoux, Aldo Segura-Cabrera, Anne Hersey, and Andrew R. Leach. ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Research, 47(D1))
2018.
[33] Mihaly Varadi, Damian Bertoni, Paulyna Magana, Urmila Paramval, Ivanna Pidruchna, Malarvizhi Radhakrishnan, Maxim Tsenkov, Sreenath Nair, Milot Mirdita, Jingi Yeo, Oleg Kovalevskiy, Kathryn Tunyasuvunakool, Agata Laydon, Augustin Zidek, Hamish Tomlinson, Dhavanthi Hariharan, Josh Abrahamson, Tim Green, John Jumper, Ewan Birney, Martin Steineg- ger, Demis Hassabis, and Sameer Velankar. AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences. Nucleic Acids Research, 52(DI):D36 D3655--375.. January 2024.
12

---

[34] Christos P. Feidakis, Radoslav Krivak, David Hoksza, and Marian Novotny. AHoJ: rapid, tailored search and retrieval of apo and holo protein structures for user-defined ligands. Bioin- formatics, 38(24):5452-544 -5533,, December 2022. Publisher: Oxford Academic.
[35] Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-sne, Journal of Machine Learning Research, 9(86) 2605, 2008.
[36] Pedro J. Ballester and W. Graham Richards. Ultrafast shape recognition to search compound databases for similar molecular shapes. Journal of Computational Chemistry, 28 28(10):771111733 2007.
_eprint: https://onlinelibaaryy wiley, cwt/doi/pd////////ooooooo/ I0. 1002/jcc.20681.
[37] G. Madhavi Sastry, Steven L. Dixon, and Woody Sherman. Rapid Shape-Based Ligand Alignment and Virtual Screening Method Based on Atom/Feature-Pait Similarities and Volume Overlap Scoring. Journal of Chemical Information and Modeling, 51(10):2455 2466, October
2011. Publisher: American Chemical Society.
[38] Hugo Guterres, Sang-Jun Park, Wei Jiang, and Wonpil Im. to Generate Reliable Holo Protein Structure Conformations from Apo Structures. Journal of Chemical Information and Modeling, 61(1):535-546, January 2021. Publisher: American Chemical Society.
Refinement
[39] Xiangying Zhang, Haotian Gao, Haojie Wang. Zhihang Chen, Zhe Zhang, Xinchong Chen, Yan Li, Yifei Qi, and Renxiao Wang. PLANET: A Multi-objective Graph Neural Network Model for Protein-Ligan Binding Affinity Prediction. Journal of Chemical Information and Modeling. 64(7):2205-2220, April 2024. Publisher: American Chemical Society.
[40] Minyi Su, Qifan Yang, Yu Du, Guoqin Feng, Zhihai Liu, Yan Li, and Renxiao Wang. Compara- tive assessment scoring functions: The casf-2016 update. Journal of Chemical Information and Modeling, 59(2):895-99) 913, 2019.
[41] Matthias R. Bauer, Tamer M. Ibrahim, Simon M. Vogel, and Frank M. Boeckler. Evaluation and optimization of virtual screening workflows with dekois 2.0 public library of challenging docking benchmark sets. Journal of Chemical Information and Modeling, 536):1447-1442, 2013. PMID: 23705874.
[42] Hannes Stärk, Octavian Ganea, Lagnajit Pattanaik, Regina Barzilay, and Tommi Jaakkola. Equibind: Geometric deep learning for drug binding structure prediction. In International Conference on Machine Learning, pages 20503-20521. PMLR, 2022.
[43] Martin Steinegger and Johannes Söding. Mmseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature biotechnology. 35(11):0026-102, 2017.
[44] David Rogers and Mathew Hahn. Extended-Commecctvity Connectivity Fingerprints. Journal of Chemical Information and Modeling, 50(5):442-754, May 2010. Publisher: American Chemical Society.
13

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification:
The main claims made in the abstract and introduction accurately reflect the paper's contributions and scope.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No
NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
is fine to include aspirational goals as motivation as long as is clear that these goals not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations the work performed by the authors?
Answer:
[NA]
Justification:
No obvious limitation needs to be discussed.
Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create a separate "Limitations" section their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution is low images are taken in low lighting. Or speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.
While the authors might fear that complete honesty about limitations might used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't 't acknowledged judgment and recognize that individual actions in favor of transparency play impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
the paper. The authors should use their best
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [NA]
14

---

Justification:
The paper does not include theoretical results.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated referenced in the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but they appear in the supplemental material, the authors are encouraged to provide short proof sketch provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that affects the main claims and/or conclusions of the paper (regardless whether the code and data are provided or not)?
Answer: [Yes]
Justification: In the experiment section and supplementary.
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g.. in the case of large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
While NeurIPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) If the contribution is primarily a new algorithm, the paper should make clear how to reproduce that algorithm. (b) the contribution is primarily a new model architecture, the paper should describe the architccture clearly and fully.
(c) If the contribution is new model (e.g., a large language model), then there should either way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reprodueibility. the case of closed-source models, may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers have some path to reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material?
15

---

Answer: [Yes]
Justification: In supplementary.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurlPS code and data submission guidelines (https://nips.cm
for more details.
While we encourage the release of code and data, we understand that this might not be possible, so "No" an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines (https
/ cppppi.ccppulli//uuuia wwwticccguie//eeieeeeeeii CoodeSubeiiie ssi wswonnolli onnolicy)))) for more details.
The authors should provide instructions on data access and preparation, including how access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification:
In the experiment section and appendix.
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core of the paper to level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, or as supplemental material.
7. Experiment statistical significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Answer: [No]
Justification: No error bars in baselines.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by error bars, confi- dence intervals, statistical significance tests, at least for the experiments that support main claims of the paper.
The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call a library function, bootstrap, etc.)
The assumptions made should be given (e.g.. Normally distributed errors).
should be clear whether the error bar is the standard deviation the standard error of the mean.
16

---

It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of enrors is not verified.
For asymmetric distributions, the authors should be careful to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative eтror rates).
If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of exccution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification:
In the experiment section and supplementary.
Guidelines:
The answer NA means that the paper does include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, cloud provider, including relevant memory and storage.
The paper should provide the amount compute required for each of the individual experimental runs well estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary failed experiments that didn't make into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics
Answer: [Yes]
Justification:
Our paper conform, in every respect, with the NeurIPS Code of Ethics.
Guidelines:
The answer NA means that the authors have reviewed the NeurIPS Code of Ethics.
If the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g., there a special consid- eration due to laws or regulations in their jurisdiction).
Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [NA]
Justification: The proposed research does not pose any foresecable societal risks, including those related misinformation, bias or fairness, data privacy, or security.
Guidelines:
The answer NA means that there is no societal impact of the work performed.
If the authors answer NA or No, they should explain why their work has no societal impact why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
17

---

The conference expects that many papers will be foundational research and not tied particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology being used intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms monitor how system learns from feedback over time, improving the efficiency and accessibility of MI..
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification: Our paper poses no such risks.
Guidelines:
The answer NA means that the paper poses such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Lieenses for existing assets
Are the creators or original owners of assets (e.g., code, data, models), used in the paper. properly credited and are the license and terms of use explicitly mentioned and properly respected?
Question:
Answer: [Yes]
Justification: All assets properly cited.
Guidelines:
The answer NA means that the paper does existing assets.
The authors should cite the original paper that produced the code package or dataset.
The authors should state which version of the asset is used and, if possible, include URL.
The name of the license (c.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (c.g., website), the copyright and terms of service of that source should be provided.
If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswi thcode. com com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
18

---

If this information is not available online, the authors are encouraged to reach out to the asset's creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [NA]
Justification: Our paper does not release new assets.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the dataset/code/mmoel as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent obtained from people whose asset is used.
At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsoureing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [NA]
Justification: Our paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included the main paper.
According the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question:
Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
Justification:
Our paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Depending the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and expect authors to adhere to the NeurlPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such the institution conducting the review.
16. Declaration of LLM usage
19

---

Question: Does the paper describe the usage of LLMs if it is important, original, or non-standard component of the core methods this research? Note that if the LLM used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
Answer: [NA]
Justification: No LLM used for method.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs any important, original, non-standard components.
Please refer to our LLM policy for what should or should not be described.
20

---

A Detailed analysis on failure modes
A.1 Overfitting to the holo pocket
In contrastive pocket-molecule learning (CPML), the pocket is typically defined as any residue within a certain distance (e.g., 6 A) of ligand atoms during training. This implies an assumption that such ligand-defined pockets are also sufficient for inference. To evaluate how closely detected pockets match the holo pocket, we quantify two related but distinct metrics: coverage. defined as Coveragel Pt; Pa) PeinPll and IoU, as defined in Equation 3 of the main text. However, shown P.
in the left side of Figure ST] BEDROC shows little correlation with coverage. but strong positive correlation with IoU. This indicates that CPML performance depends more on the spatial consistency between the detected and holo pockets than on the overall inclusion of grgaund-eighboring residues. This suggests overfitting effect-models may rely heavily on ligand-defined pocket extraction patterns rather than learning generalizable structural representations. In contrast, as shown on the right side of Figure [S] our tri-modal alignment substantially improves early enrichment for targets with moderate loU values (0.4- - 0.6), except for a few with very low IoU (< 0.3), where no significant correlation is observed with either coverage or IoU.
Coverage (r=0.14, p=0.17)
Coverage (rm-0.16, p=0.11]
0.5
0.7
1.0
BEDROC (a- -80.0 80.5)
BEDROC (a=80.55
0.6
0.4
0.4
0.2
0.2
0.0
0.0
Intersection over Union (n=0.41, 0.41, p p=2.00e-05)
Intersection over Union (r- D.17, p- 0.08)
Figure S1: Correlation between BEDROC (a = 80.5) and the IoU / coverage of the closest detected pocket the holo pocket on each target. Left: contrastive pocket-molecule learning; Right: Tri- modal alignment (ours).
A.2 Low-IoU target cases
In Figure SI several points exhibit full coverage but lack perfect overlap with the holo pocket (i.c.. IoU 1). One such example, HS90A (PDB ID: IUYG), visualized on the left side of Figure S2 In this case, the ligand occupies only part of larger cavity; the detected cavity is superset of the holo pocket. Although this cavity contains sufficient structural information, CPML is disrupted by the additional irrelevant regions- motivvaing our use of the cavity modality to better capture spatial features. Another representative case, shown on the right side of Figure S2 THRB (PDB ID: 1YPE), involves a cavity that only partially covers the holo pocket; the ligand actually spans across two detected cavities. In such scenarios, selecting only one cavity- if correct-may be insufficient to fully characterize the binding pocket. This observation motivates our pocket enlargement (from 6 to 10 Å) strategy.
A.3 Pocket external shape and ligand shape
Another potential failure mode of ieeeneed pocket extraction arises from how residues are selected: any residue within a fixed distance of any ligand atom is included, resulting in a coarse, ligand-shaped approximation of the pocket's outer boundary. To illustrate this effect, the ligands from Figure S2 are shown with their corresponding holo pocket surfaces in Figure S3
21

---

Figure S2: Target cases with low IoU. The protein structure around the ligand is shown in blue, the ligand in green, and the closest detected cavity orange. Left: HS90A (PDB ID: IUYG), where the detected cavity is a superset of the holo pocket. Right: THRB (PDB ID: YYPE), where the detected cavity only partially covers the pocket; an additional cavity corresponding to the remaining pocket region is shown in wheat.
Figure S3: Ligand (atom spheres colored cyan) within the pocket's external surface (rendered as tan mesh). Left: HS90A (PDB ID: IUYG); Right: THRB (PDB ID: TYPE).
Table S1:
Correlation between CPML and 3D similarity search methods using the holo ligand, along with their enrichment performance. Both Pearson (PRS) and Spearman (SPR) correlations evaluated on the active set, the full set, and the top 1 % of molecules ranked by CPML.
Actives
Full
Top % (CPML)
BEDROC (a 80.5)
Similarity
EF1% %
PRS
SPR
PRS
SPR
PRS
SPR
USR
0.0004
0.0024
0.0014 0.0012
0.0011 0.0008
0.0103 0.0048
0.0062
2.74
0.0514
PhaseShape
0.0075
0.0066
0.0040
8.29
0.1484
To further examine the shape dependence of CPML, we analyze its correlation with two 3D molec- ular similarity search methods: Ultrafast Shape Recognition (USR) (36), based on atom disttnit descriptors, and PhaseShape (37), based on pairwise volume overlap. As shown in Table ST] CPML scores show no meaningful correlation with either method across the active set, the full set, or the top 1 1 of molecules ranked by CPML. This suggests that CPML. may not account for the external pocket shape in enriching molecules with similar 3D shape.
22

---

B Implementation details
B.1 Pseudo code
Algorithm Cavity Extraction
Require:
protein structure coordinates Zn, co-crystallized ligand coordinates I' {C detector(2n)
each cavity: set of alpha-sphere centers
for = 1 to S do
Re +- {r E residues Er, E C(+), | ell s sd)
residue-level pocket
end for
Return: candidate pockets { { )am
Algorithm Tri-Modal Contrastive Alignment
Require:
pocket encoder F, cavity encoder Fs ligand encoder G, candidate cavities {P(+)] holo pocket Pt, positive IoU threshold Tpos, negative IoU threshold Tneg for each protein-ligand complex (In,l, Pi) do
J==1.
ToU Pa) [PUPP
compute overlap ratio between holo pocket and candidate
P. { with [PU 2 Tpss
sample one positive cavity
{P-} < random half of (P(") (P) p(() S Tneg
D sample hard negatives
LCL (F(((,, g(t))
Lp. (F.(P.), G(t)) + (P.)),
Update parameters of F, Fo, 9 by minimizing LCL end for Return: pretrained pockt encoder F, pretrained cavity encoder F,,, pretrained ligand encoder Ç
Algorithm 3 Training Cross-Attention Adapter for Cavity Aggregation
Require: pretrained cavity encoders (F.) pretrained ligand encoder G, trainable adapter (atten- tion weights and projection layer), temperature t, bias b, weight À
for each example (1, (P! ss ) do
es 5 g())
for 8 = 1,.... .,... do
(0) - (P!
g(t) (e,, c("))) /t end for
compute dot dot-product logits
al4) t- exp(E("))
1=1 / exp(e(i)) Vs
softmax attention
& E (e) é - projection(e:)
aggregate cavities
compute target distribution [$1. ... ss]: if complex sample then
8, for the true cavity, otherwise else if activity-only sample then
S, pretrained AANet cavity scores
end if
C'KL. < E5= 8, log( (
attention supervision
Fe <- log(1 + exp( & + b))
Lagy < C... Ckr.
Update adapter parameters to minimize Lagg end for
Return: trained adapter parameters
B.2 ChEMBL data processing
Molecule filtering:
Removed salts or kept only the largest fragment
23

---

Kept molecules with molecular weight between [100, 800]
Removed those containing atoms other than [H, C, N. O, F, CI, Br. I, S, B, Se]
Filtered out molecules with unbranched long chains containing atoms or more.
Activity filtering: We retained activity records with:
Confidence score
Assay type functional or binding
Standard type E ['Ki', 'IC50', 'Kd', 'EC50', 'ED50', 'AC50", 'XC50"]
Values converted to molar units and filtered to lie within the -log10 range [5, 12]
Protein targets were mapped to UniProt IDs and matched with AlphaFold structures. Targets without available predicted structures were discarded.
Target mapping:
Contrastive supervision masking: Each known protein-ligand activity pair was recorded using its UniProt ID and InChI-key. During training, we masked non-diagonal entries that correspond to active (positive) pairs to ensure that true positive pairs are never treated as negatives, even if they are not aligned in the current batch. This masking strategy ensures correct supervision and avoids misleading the model during contrastive learning.
B.3 Benchmark details
We summarize the PDB entries used as apo structures for LIT-PCBA in Table S2 apo structures and AF2-predicted structures were aligned to each reference holo PDB using structaliga module from the Schrödinger Suite. The resulting alignment scores and root-mean- square deviations (RMSD) are reported in Table for both types of structures. To reduce manual intervention and enable scalability, adopted fully automated pipeline for the apo (blind) setting. Notably, we retained only non-redundant, non-homologous protein complexes from the apo structures, which increases the overall difficulty of the task and better reflects realistic large-scale virtual screening scenarios.
Experimental
Table S2: PDB entries selected as apo structures for LIT-PCBA targets. Bold chain IDs indicate the primary chain(s) of interest.
Target Name
UniProt ID
PDB ID
Selected Chain(s)
Comment
Non-redundant protein complex retained
ADRB2
P07550
9chv
A, B,
ALDHI
PO0352
4wj9
ESR1 (ago/ant)
PO3372
2b23
A
FENI
P39748
5zod
GBA
PO4062
3gxd
A
Ligand in holo (6ADG) binds at the interface of two homologous chains
IDHI
075874
1t01
A, B
KAT2A
Q92830
5trm
A
Non-redundant protein complex retained
MAPK1
P28482
4iz7
A, B
PKM2
P14618
1zjh
PPARG
P37231
Iprg
A
Non-redundant protein complex retained
TP53
P04637
Ikzy
A,
24

---

Table S3: Alignment and RMSD for AF2 and apo structures across LIT-PCBA targets.
AF2
Apo
Target
Ref PDB
Score
RMSD
Score
RMSD
ADRB2
3p0g
0.106
1.609
0.238
2.398
3pds
0.165
2.032
0.314
2.793
3sn6
0.107
1.592
0.225
2.336
4lde
0.203
2.127
0.224
2.322
4ldl
0.261
2.540
0.428
3.261
4ldo
0.210
2.279
0.408
3.142
4qkx
0.232
2,400
0.362
2.998
6mxt
0.201
2.218
0.323
2.744
mean
0.186
2.100
0.315
2.749
ALDHI
4wp7
0.004
0,302
0.000
0.090
4wpn
0.004
0.301
0.001
0.126
4x41
0.005
0.329
0.002
0.220
5ac2
0.006
0.345
0.002
0.211
512m
0.004
0.307
0.001
0.136
512n
0.005
0.329
0.001
0.154
512o
0.009
0,463
0.004
0.301
5tei
0.005
0.346
0.002
0.232
mean
0,005
0.340
0.002
0,184
ESRI_ago
112i
0.018
0.670
0.018
0.665
2blv
0.018
0.666
0.010
0.495
2b1z
0.016
0.628
0.009
0.481
2p15
0.041
1.009
0.020
0.706
2q70
0.119
1.721
0.063
1.249
2qr9
0.032
0.898
0.016
0.622
2qse
0.028
0.842
0.027
0.779
2qzo
0.025
0.793
0.022
0.689
4ivw
0.045
0,947
0.007
0.412
4pps
0.018
0.662
0.010
0.483
5drj
0.021
0.726
0.009
0.481
5du5
0.015
0.616
0.012
0.551
5due
0.028
0.838
0.008
0.456
5dzi
0.032
0.756
0.007
0.419
Selc
0.025
0.788
0.010
0.489
mean
0.032
0.837
0.017
0.598
ESR1 ant
1xpl
0.086
1,419
0.076
1.374
1xqc
0.088
1,425
0.092
1.511
2ayr
0.100
1.546
0.104
1.615
2iog
0.083
1.321
0.075
1.367
2iok
0.089
1.208
0.070
1.174
2ouz
0.090
1.473
0.093
1.521
2pog
0.050
1.059
0.043
0.859
2r6w
0.047
1.045
0.033
0.857
3dt3
0.130
1.783
0.101
1,590
5aau
0.084
1.452
0.080
1.412
5fqv
0.098
1.550
0.074
1.361
5t92
0.008
0.440
0.007
0.405
5ufx
0.095
1.528
0.083
1,338
6b0f
0.062
1.123
0.041
1.014
6chw
0.106
1.567
0.103
1.607
mean
0.081
1.329
0.072
1.267
FENI GBA
5fv7
0.056
1.181
0.038
0,970
2v3d
0.014
0.549
0.026
0.778
2v3e
0.012
0.510
0.027
0.752
2xwd
0.006
0.376
0.028
0.727
2xwe
0.013
0.557
0.030
0.770
25

---

AF2
Apo
Target
Ref PDB
Score
RMSD
Score
RMSD 4
3rik
0.016
0.567
0.017
0.615
3ril
0.017
0.599
0.011
0.468
mean
0,013
0.526
0.023
0.685
IDHI
413k
0.192
2.180
0.409
3.184
4i31
0.233
2.378
0.318
2.759
4umx
0.209
2,238
0,310
2.746
4xrx
0.172
2.068
0.388
3.107
4xs3
0.190
2.128
0.352
2.926
5de1
0.165
2.017
0.367
2.876
5157
0,209
2.276
0.397
3.127
5158
0.155
1,954
0.368
3.028
5lge
0.103
1.589
0.258
2.534
5sun
0.189
2.117
0.320
2.787
5svf
0.274
2.602
0.334
2.844
5tqh
0.125
1.757
0.304
2.702
6adg
0.179
2.074
0.372
2.987
6b0z
0.193
2.143
0.366
2.979
mean
0.185
2.109
0.347
2.899
KAT2A
5h84
0.052
1.137
0.022
0.736
5h86
0.085
1,458
0.045
1,061
5mlj
0.016
0.637
0.338
2.782
mean
0.051
1.077
0.135
1.526
MAPK1
1pme
0.069
1.307
0.040
0,990
2ojg
0.063
1,250
0.040
1.005
3sa0
0.082
1.424
0.047
1.085
3w55
0.074
1.358
0.101
1.590
4qp3
0.055
1.169
0.038
0.969
4qp4
0.046
1.069
0.071
1.329
4qp9
0.051
1.132
0.053
1.145
4qta
0.030
0.859
0.055
1.177
4qte
0.077
1.391
0.060
1.195
4xj0
0.077
1,390
0.061
1.235
4zzn
0.056
1.187
0.036
0.949
5ax3
0.059
1.190
0.075
1.365
5buj
0.065
1.271
0.109
1.648
5v62
0.063
1.246
0.088
1.470
6g9h
0.057
1.189
0.033
0.902
mean
0.062
1.229
0.060
1.204
PKM2
3gqy
0.141
1.837
0.030
0.741
3gr4
0.032
0.794
0.197
2.161
3h60
0.175
2.052
0.038
0.795
3me3
0.028
0.758
0.186
2.098
3u2z
0.148
1.759
0.035
0.778
4g In
0.025
0.776
0.127
1.773
4jpg
0.031
0.689
0.038
0.786
5x1v
0.032
0.724
0.045
0.829
5xlw
0.029
0.679
0.038
0.847
mean
0.071
1.119
0.082
1.20/
PPARG
Izgy
0.056
1.163
0.073
1.349
2i4j
0.044
1.047
0,009
0,479
2p4y
0.017
0.644
0.018
0.671
2q5s
0.019
0.695
0.038
0.944
2yfe
0.026
0.808
0.023
0.764
3blm
0.021
0.724
0.035
0.937
3hod
0.045
1.058
0.007
0.415
3r8a
0.025
0.797
0.032
0.893
26

---

AF2
Apo
Target
Ref PDB
Score
RMSD
Score
RMSD 4
4ci5
0.042
0.947
0.026
0.801
4fgy
0.038
0,967
0.061
1.211
4prg
0.068
1.298
0.030
0.853
5tto
0.018
0.665
0.017
0.659
5two
0.021
0.731
0.091
1.509
5y2t
0.026
0,810
0.066
1.284
5z5s
0.022
0.738
0.049
1.072
mean
0,033
0.873
0.038
0.923
TP53
2vuk
0.010
0.508
0.014
0.582
3zme
0.010
0,498
0,015
0.607
4ago
0.008
0.440
0.011
0.521
4agq
0.008
0,442
0.011
0.520
5g40
0.008
0.458
0.014
0.585
5oli
0.006
0.389
0.011
0.534
mean
0,008
0.456
0.013
0.558
overall mean
0.082
1.199
0.101
1.285
For details on the DUD-E benchmark, see the supplementary information of [38 and 9
B.4 Hyperparameters
Table S4
summarizes the full set of hyperparameters used to reproduce the results of AANet.
Table S4: Training and evaluation hyperparameters used in this study.
Data processing
Max. No. ligand conformers
10
Min. RMSD among ligand conformers
Pocket radius
10
loU for positives
0.5
loU for negatives
0.1
Training
Learning rate
0.001
Batch size
48
Random seed
Model hyperparameters
Following DrugCLIP
Cavity negative ratio
0.5
Max. epochs
200
Early stopping
10/5
Loss logit scale
log(10)
Loss logit bias
-10
Adapter softmax temperature
Use fp16
True
Testing
Max. No. conformers
Results from multiple pockets (LIT-PCBA)
Max
001 pdf
001 pdf
27

---

B.5 Experiments compute resources
To provide comprehensive estimation of computational resources, we report the main training and inference costs in SS The benchmark evaluations include combination of targets from DUD-E and LIT-PCBA under multiple structural conditions, including holo, apo, and AF2-derived proteins, as well as different pocket definitions: oracle, annotated, and blind. Each structural condition requires repeated docking and evaluation runs, substantially increasing the cumulative compute burden. In addition to the experiments reported in the main paper, we also performed number of pilot and ablation studies during model development that are not included in the final results but contributed to the overall resource consumption.
Table S5: Compute resource disclosure.
Experiment
Resource
Run time
Unit
Training Alignment phase Training Aggregation phase
4 NVIDIA A A100 (80 GB)
h
per
4 x NVIDIA A100 (80 GB)
per run
Model testing (single benchmark)
1 X NVIDIA A100 (80 GB)
1-5 min
per benchmark
Docking
128-core CPU server
3-7 days
per benchmark
Baseline evaluation (per DL model)
1 x NVIDIA A100 (80 GB)
minutes- day
per benchmark
B.6
Metrics
We evaluate virtual screening performance using three complementary measures that capture both overall ranking quality and carly-retrieval effectiveness.
BEDROC applies an exponential weight to ranks so that top-ranked actives contribute most to the score. Let R; be the 1-based rank of the i-th active among N compounds, and let R, Nact/ /N be the active fraction. Define
1 1-6 e Ca/N -
Za Ra
(SI)
Then
Mast
aRR/N
Ra sinh(a/2) cosh(a//2) vosh(a/2 2 aRR) + 1 ca(l-R,)) R.))
BEDROC,
(S2)
Za
In our experiments we set 80.5.
Enrichment Factor (0) quantifies how many more actives are found in the top 8% of the ranked list compared to random selection. If N is the total library size, Nact the total number of actives, and n5% the number of actives among the top ls [6N/100) compounds, then
71,%
k
118% N
A:
EF 5%
(S3)
100
N act
1 Nact
N
AUROC measures the probability that randomly chosen active is scored higher than randomly chosen decoy. Let TP(t) and (t) be the counts of actives and decoys above score threshold t, with Nact and Nase their totals. Then
TP(t) Nact
FP(t) Naec
TPR(t)
FPR(t)
(S4)
and
AUROC = S TPR(FPR '(u)) du.
(S5)
All metrics computed independently for each target and then averaged over the benchmark.
28

---

B.7 Docking on LIT-PCBA
Due to the high computational cost of docking on LIT-PCBA, we followed PLANET 39 and performed docking using single holo structure, as shown in Table S6 with experimental apo and AF2-predicted structures aligned accordingly.
Table S6: PDB codes of protein structures used in the LIT-PCBA virtual screening dataset. Asterisks (*) indicate targets with no available apo structure.
Target Name
PDB ID
ADRB2
4LDE
ALDHI
5L2N
ESRI LoRl_agonist
2QZO
ESR ESRI_antagee _anttgonnistt
5UFX
GBA
2V3D
FEN1
5FV7
IDHI
4UMX
KAT2A
SMLJ
MAPK1
4ZZN
MTORCI
4DRI
OPRKI*
6B73
PKM2
3GR4
PPARG
3B1M
TP53
37ME
VDR
3A2J
B.8
Baseline implementation details
We compare against representative baselines from two categories.
Docking & Rescoring
Targets were prepared using the prepwizard module with default settings. Molecules were processed using the ligprep module to generate up to 32 tautomers and stereoisomers. Docking was performed using the glide module with grid box radius of 10 Å, centered either at the co- crystallized ligand or the closest detected cavity center (in the annotated setting), using Standard Precision mode and default parameters. All modules were from the Schrödinger Suite 2024-1 distribution. The top-ranked conformer for each molecule was retained and subsequently used for rescoring baselines.
Glide (SP) [
RTMScore 22 Implementation obtained from the official GitHub https://github com/ (00068//TMScore/ and their pretrained weights on the PDBbind-v2020 [31] general set. The authors excluded any complexes overlapping with the PDBbind-v2020 core set and the CASF-2016 [40] benchmark (reducing from 19,443 to 19.149 complexes), but did not apply filtering against DUD-E or LIT-PCBA. Following the repository defaults, pockets are defined as all receptor atoms within 10 À of the ligand, the cavity in the annotated setting, along with the default scoring scheme.
We downloaded the code and model weights (trained on PDBscreen, with all proteins from DUD-E and DEKOIS2.0 [4] excluded from the training data, but not those from LIT-PCBA) from hattps://githii com/ CADDH/ EeopH///euoooeeee Each molecule's 'ss own docking pose was used to define its pocket, i.c., receptor residues within A. Scores were then obtained using the default inference configuration.
EquiScore 211
Docking-free baseline
BinkBind 223 We downloaded the official implementation from https://githie com/
Iuwei0917/Ter www.099977/aaniinnn. along with their pretrained weights on PDBbind-v2020 [31]. Following the EquiBind [42] time split, complexes deposited before 2019 (17,787 after RDKit filtering) consti- tuted the training set, those from 2019 (968) the validation set, and those after 2019 (363) the test set, without deduplication against DUD-E or LIT-PCBA. We followed the original settings for all preprocessing and hyperparameters. Pockets were detected using P2Rank (28), with 20 À radius
29

---

around each predicted center. In the annotated setting, when a true pocket was available, we computed its centroid and selected the single P2Rank pocket whose center was closest. In the blind setting. where no ligand was provided, we scored all P2Rank pockets and retained the maximum predicted affinity per compound.
DrugCLIP [3: We used the official DrugCLIP implementation from attps://githuu com/ bowen-gao/ Baai-ggg/Drrruu DrugCLIP The model was fine-tuned on the PDBbind-v2019 general set, with all complexes overlapped with those in DUD-E or LIT-PCBA removed to ensure zero-shot evaluation. All other hyperparameters and data processing followed the authors' original settings.
C Additional results
C.1 Benchmarking computational cost on the LIT-PCBA dataset
As our dual-tower backbone does not involve fusion prior the adapter and supports embedding reuse, AANet introduces minimal overhead beyond DrugCLIP while providing significantly improved performance, particularly in unbound settings. As shown in Table [S7] the majority of runtime during the scoring phase arises from data loading and CPU-GPU communication, rather than model inference itself. On typical commercial-scale libraries, AANet maintains ultra-fast screening throughput comparable to DrugCLIP.
Table S7: Comparison of computational cost across methods. *Sharing cost refers to preprocessing that can be reused. **PCBA-One corresponds to the subset of 2.8M complexes.
Method
Sharing Cost"
PCBA-Qne** (2.8M)
PCBA-Full (10M)
-CBA-Apo-Bllidd (100M)
Overall Cost
Giide
Ligttep hr (930 molk/s)
3 days
11 days
3.6 months
days - 3.6 monits
Preprcessinn:: 3 hrs (2471)
RTMScoe
Inference: 8.2 his (9483)
11.2
17 days
11.2 17 days
Prepmeessing: 2.7 hrs (2911)
EquiScore
Inferenee: 8.1 his (966))
11 hrs 29 hrs
16.7 days
10.8 hrs 16.7 days
TankBind
7 has
25 hrs
10.1 days
has - 10.4 days
DrugCLIP
Mol: 270 (10,37k))
Pocket: (96/)
Mol: 270 (10,3706)
AANer
Pocket: 1-3 (36/)
15
15
~300
C.2 AUROC under annotated and blind settings
Table S8 reports the AUROC of all methods on DUD-E and LIT-PCBA across annotated and blind structural settings. While AUROC reflects overall ranking quality, it less indicative of early retrieval performance, which is more critical for virtual screening given practical experimental costs.
C,3 Results for holo full sets
We benchmarked AANet and the baselines under conventional holo settings, where holo complexes were provided and pockets were defined by the co-crystallized ligands, as shown in Table SS
C.4 Results on the DEKOIS dataset
we mapped the DEKOIS targets to UniProt ID and successfully downloaded 77 target structures from the AlphaFold Database, excluding virus-related targets that are not available HIIIIP& & HIV IRT: P03366, LARRS-CoV; P0C6X7, NA: P27907).
As shown in Table STO, surpasses traditional docking baselines under holo pocket settings (italic) when tested under the most challenging apo-pred blind setup (bolded).
method consistently outperforms DrugCLIP across all settings, and
C.5 Results on the DUD-E dataset under de-redundant / de-homologous setting
On PDBbind, we removed duplicate complexes based on structure IDs, following prior work to ensure fair comparison. However, this may not fully eliminate target-level redundancy. For some of baselines, duplicates were not removed (Appendex [.8). On ChEMBL, we filtered data using UniProt IDs, which more reliably removes overlapping targets.
30

---

Table S8: AUROC performance on DUD-E and LIT-PCBA. Each method is evaluated on three structural subsets: holo, apo (experimental), and apo (predicted), under both annotated and blind
settings. Row colors indicate method type: Docking & Rescoring. Docking-free baseline... and Proposed method
apo-exp (annot)
apo-pred (annol)
apo-exp (blind)
apo-pred (blind)
Method
Holo
DUD-E
38)
Glide-SP
0.6054
0.5461
0.5588
RTMScore
0.7211
0.6077
0.6246
EquiScore
0.7257
0.6478
0,6631
TankBind
0.7872
0.7765
0.8006
0.7745
0.7897
DrugCLIP
0.8133
0.7518
0.7826
0.6134
0.5798
AANet
0.8963
0.8744
0.8752
0.8593
0.8617
LIT-PCBA (n 12)
Glide-SP
0.5078
0.5070
0.5037
RTMScore
0.5245
0.5161
0.5324
EquiScore
0.5800
0.4694
0.5342
TankBind 0.6110
0.6043
0.6121
0.61 19
0,5942
DrugCLIP 0.5742
0.5851
0.5635
0.5006
0.4805
AANet
0.5621
0.5737
0.5640
0.5750
0.5641
Table S9: Performance comparison DUDE and LIT-PCBA benchmarks under conventional holo
setting.
DUDE
LIT-PCBA
Method
BEDROC (a 80.5)
BEDROC (a 80.5)
AUROC
EF 1%
AUROC
EF 1%
Docking Rescoring
Glide-SP
0.7670
0.4070
16.18
0.5315
0.0400
3.41
Vina
0.7160
7.32
0.5693
0.0370
1.71
NN-score
0.6830
0.1220
4.02
0.5570
0.0250
1.70
RFscore
0.6521
0.1241
4.52
0.5710
1.67
Pafnucy
0.6311
0.1650
3.86
5.32
OnionNet
0.5971
0.0862
2.84
RTMScore
0.7529
0,4341
27.10
0.5247
0.0388
2.94
EquiScore
0.7760
0.4320
17.68
0.5678
0.0490
3.51
Docking-free
TankBind
0.7509
0.3300
13.00
0.5970
0.0389
2.90
Planet 0.7160
8.83
0.5731
3.87
DrugCLIP 0.8093
0.5052
31.89
0.5717
0.0623
5.51
AANet 0.8510
0.5592
36.05
0.5353
0.0677
5.13
To further address potential leakage, conducted additional experiment by using MMseqs2 to identify and remove any training proteins (from PDBbind and ChEMBL) containing chains with > 90% sequence identity any DUD-E structure target sequences. We then compared method and DrugCLIP under both the original and this more stringent de-redundant / de-homologous setting. As shown in Table SII our method maintains robust performance even after removing homologous sequences, demonstrating better generalization.
43
C.6 Comparison with ligand- -baed methods
ligand-based virtual screening (LBVS)) can bypass the need for protein structural information and particularly useful when high-quality ligands are available when protein structures unreliable.
31

---

Table S10: Performance on DEKOIS (77 targets) under holo, apo-pred-anme annottnne (the cavity detected by FPocket that is closest to the holo pocket), and spo-pred-ble settings.
AUROC
AUROC
AUROC
BEDROC
BEDROC
BEDROC
EF
EF
EF EF.05%%
Method
(holo) 1 (apo-annot)
(apo- blind)
(holo) 1
(apo-annot)
(apo-hlind)
(holo)
(apo-annat)
(apo-blind)
Vina
2.58
GOLD
10.32
Glide
DrngCLIP
0.7967
0.7075
0.4355
0.532
0.322
0.0313
20.62
12.12
0.92
AANet
0.8785
0,8624
0.7708
0.6446
0.5824
0.3891
23.26
20.91
13.33
Table S11: Performance on DUD-E under de- redunnann and de- de-homologous (90%) settings.
DEDROL
DEDROR
DEDROR
EF01%
Slidd
Dig
0.5157
0.1345
0.5435
0.5914
0.3975
55.20
22:70
21:16
17.05
11:75
(e-hoondoogges)
DrgeLLIP
0.3940
0.3325
0.2134
0.0007
0.1206
24.00
13.56
12. 5.
6.51
7.16
AANei
0.5764
35
AANei
0L3WAT
033247
0,5475
0.3055
015477
34.76
38.34
4450
However, LBVS is often more straightforward when good query ligands are available (e.g., potent, exogenous ligands), but may be less effective in identifying novel chemotypes or in the absence of relevant prior ligands. In contrast, SBVS leverages the protein's 's structural context, which can help discover structurally diverse actives but depends on structure quality.
We evaluated several widely-used LBVS methods, covering both 2D and 3D paradigms, including ECFP (2D topology-basedl, USR (3D maape-based)) and Shape from Schrödinger suite (3D shape overlap). The results are summarized in Table ST2]
Table S12: Performance of common LBVS methods on DUD-E benchmark.
Method
EF@ 1
BEDROC (a 80.5)
USR
36
2.74
0.051
PhaseShape 37
8.29
0.138
ECFP4
26.61
0.414
AANet (apo-blind)
37.19
0.576
The notably strong performance of ECFP4 is likely due to the fact that many actives in virtual screening benchmarks such as DUD-E share similar topological scaffolds, whereas decoys are specifically designed to have similar physicochemical properties but divergent topologies. As result, ECFP4 can effectively distinguish actives from decoys based topological similarity alone, leading to higher enrichment factors. However, such advantages may not always generalize to real-world screening scenarios, where novel actives are often needed.
D
Additional Discussion
D.1 Selection of the pocket extraction radius
This choice of 10 Á pocket extraction radius was based on quantitative analysis on the PDBbind dataset, supported by the following three key findings:
1. Coverage of original holo pockets: The oracle 10 À cavities, which are defined those with the highest overlap (IoU) with holo pockets, fully cover 75.38% of the original 6 Å holo pockets, and 90.94% achieve > 85% coverage. This indicates that 10 A cavities provide a reliable approximation of the ground-trum pocket regions.
2. Tradeoff between feature integrity and computational cost: The average atom count in 10 Á oracle cavities is 612.68 + 240.75, over 3x larger than the 6 Á holo pockets (199.32 55.04). Since computational cost scales non-linearly with the number of input atoms, we uniformly downsample pockets to < 256 atoms during training to ensure fair comparisons across different pocket definitions.
32

---

Larger regions would require more aggressive downsampling, which may compromise spatial features or increase computational overhead. Thus, 10 Á represents a practical upper bound that balances representational fidelity with efficiency.
3. Avoiding bias from over-extension: Extending the radius beyond 10 Á risks incorporating off-target cavities, especially on cluttered protein surfaces. As our 10 Á is defined by minimal atom- to-atom distance (not center-based), already captures ample structural context. Further expansion would dilute the binding signal and potentially introduce irrelevant patterns.
Thus, the 10 Å threshold strikes a practical balance: it ensures high coverage of the ground-truth binding pocket, preserves structural integrity, and avoids unnecessary computational overhead or learning from irrelevant patterns.
D.2 The role of holo pocket in tri-modal contrastive learning
The alignment with holo pockets plays critical and bidirectional role in tri-modal contrastive framework, motivated by the following:
1. Holo Cavity Guidance for learning from noisy input: Holo pockets, shaped by actual ligand binding, serve as reliable supervisory anchors (as stated by the reviewer). Aligning detected cavities with holo pockets helps the model extract meaningful features even from noisy or imprecise cavity detection.
2. Cavity + Holo Addressing structural uncertainty and generalization: Holo pockets are biased toward specific ligand series (Appendix A), while other actives may bind elsewhere. By aligning cavities (which are ligand-agnostic) to holo pockets, the model learns features that are spatially intrinsic to the protein. This improves the model's robustness to structural uncertainty and enhances generalization. This alignment also boosts performance on holo structures, since the learned features are less tied to oggand-specifer conformations.
3. Cavity <> Holo Dual modeling of general and specifie signals: The model jointly learns ligand-independent (intrinsic to proteins) and ligand-specific pocket representations, which improves robustness. This enables stronger performance across both holo and apo structures, not just mitigating the performance gap but outperforming DrugCLIP both cases.
In summary, holo-cavity alignment essential for modeling both precise and generalizable binding patterns, supporting our goal of robust virtual screening under structural uncertainty.
D.3 Strengths and limitations of pocket detection
While our current framework employs Fpocket for cavity detection during training. AANet demon- strates robustness across different cavity detectors. Although our model uses Fpocket to extract training cavities, the downstream performance remains stable across different cavity detection tools (Table 4 These conventional detectors grounded in physicochemical definitions of binding pockets, such as surface concavity, void volume, and geometric constraints. The consistency across tools suggests that AANet is not overfitting to specific detection algorithm, but instead learns underlying structural priors intrinsic to proteins, such as spatial enclosures or clefts.
Alignment with practical structure-based pipelines: Our method follows the typical structure- based virtual screening pipeline, where pocket detection and binding assessment are distinct stages. This modularity aligns with standard workflows in drug discovery and allows our method to plug into existing infrastructure while remaining adaptable to alternative cavity detectors.
Physically meaningful supervision: By grounding learning on cavities defined via physical heuristics (e.g., void detection), we introduce inductive bias from medicinal chemistry intuition into the model. This helps prevent overfitting to spurious binding patterns and encourages the model to focus on biophysically plausible pockets. Concerns about physical plausibility have recently gained attention in the structure prediction and docking communities, highlighted by works such as PoseBusters in deep learning- docking. This emphasis on biophysical realism equally relevant to virtual screening and motivates the design of our method.
33

---

Although we currently do not explore end-fo-end cavity prediction, which is promising direction, especially on novel targets including flexible proteins. As datasets grow and modeling techniques evolve, we believe this integration could offer even more robust and generalizable solutions.
Limitation in flexible disordered proteins: We acknowledge that for highly flexible intrinsi- cally disordered proteins, where well-formed pockets are absent and ligand-induced fit is essential, existing pocket-based SBVS methods, including ours, face inherent challenges. Our work primarily addresses uncertainty in binding site localization, not full flexible binding modeling.
34


Reviews:
[{"review_id": "Icdk4aE8kT", "reviewer": "Reviewer_ETw8", "summary": "This 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.", "questions": "1. 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.", "limitations": "N/A", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "The authors have addressed my concerns during the rebuttal. In addition, based on the comments of other reviewers, I'd like to keep the positive score.", "rebuttal": "> W1: Lack of discussion on computational efficiency\n\nTo address the reviewer's concern, we provide a direct comparison of overall computational cost among representative methods on the LIT-PCBA dataset, using a single NVIDIA A100 80GB GPU and a 128 vCPU server.\n\n| Method     | Sharing Cost*                          | PCBA-One** (2.8M) | PCBA-Full (10M) | PCBA-Apo-Blind (100M) | Overall Cost         |\n|------------|----------------------------------------|------------------|------------------|-------------------------|-----------------------|\n| Glide      | LigPrep: 1 hr (930 mols/s)                     | 3 days           | 11 days          | 3.6 months              | 3 days – 3.6 months   |\n| RTMScore  | – | Preprocessing: 3 hrs (247/s) + Inference 8.2 hrs (94/s) | 11.2 hrs +  29.5 hrs | 17 days | 11.2 hrs - 17 days |\n| EquiScore | – | Preprocessing: 2.7 hrs (291/s) + Inference 8.1 hrs (96/s) | 11 hrs + 29 hrs | 16.7 days | 10.8 hrs - 16.7 days |\n| TankBind   | – | 7 hrs              | 25 hrs             | 10.4 days               | 7 hrs – 10.4 days       |\n| DrugCLIP   | Mol: 270 s (10,370/s), Pocket: 1–2 s (86/s) | –                | 15 s              | 15 s                     | ~300 s               |\n| AANet      | Mol: 270 s (10,370/s), Pocket: 1–3 s (36/s) | –                | 15 s              | 15 s                     | ~300 s               |\n\n\\*: Cost that may be shared across subsequent virtual screening (VS) projects;\n\\*\\*: One highest-resolution PDB structure was selected for each subset (cf. Supplementary Section B.6).\n\nAs our dual-tower backbone does not involve fusion prior to the adapter and supports embedding reuse, our method (AANet) introduces minimal overhead beyond DrugCLIP while providing significantly improved performance, particularly in unbound settings. The majority of runtime during the scoring phase arises from data loading and CPU–GPU communication, rather than model inference itself. **On typical commercial-scale libraries, AANet maintains ultra-fast screening throughput** comparable to DrugCLIP.\n\n> W2: Clarification on the 10 Å pocket extraction radius\n\nWe appreciate the reviewer’s concern regarding the selection of the 10 Å threshold for pocket extraction. This choice was based on quantitative analysis on the PDBbind dataset, supported by the following three key findings:\n\n1. **Coverage of original holo pockets**: The oracle 10 Å cavities, which are defined as those with the highest overlap (IoU) with holo pockets, **fully cover 75.38% of the original 6 Å holo pockets, and 90.94% achieve ≥ 85% coverage**. This indicates that 10 Å cavities provide a reliable approximation of the ground-truth pocket regions.\n\n2. **Tradeoff between feature integrity and computational cost**: The average atom count in 10 Å oracle cavities is **612.68 ± 240.75, over 3× larger** than the 6 Å holo pockets (199.32 ± 55.04). Since computational cost scales non-linearly with the number of input atoms, we **uniformly downsample pockets to ≤ 256 atoms** during training to ensure **fair comparisons across different pocket definitions**. Larger regions would require more aggressive downsampling, which may **compromise spatial features or increase computational overhead**. Thus, 10 Å represents a practical upper bound that balances representational fidelity with efficiency.\n\n3. **Avoiding bias from over-extension**: Extending the radius beyond 10 Å **risks incorporating off-target cavities**, especially on cluttered protein surfaces. As our 10 Å is defined by minimal atom-to-atom distance (not center-based), it already captures ample structural context. Further expansion would dilute the binding signal and potentially introduce irrelevant patterns.\n\n**Thus, the 10 Å threshold strikes a practical balance**: it ensures high coverage of the ground-truth binding pocket, preserves structural integrity, and avoids unnecessary computational overhead or learning from irrelevant patterns.\n\n> Q1: What is the role of aligning with the 'holo pocket' in tri-modal contrastive learning?\n\nWe thank the reviewer for this insightful question. The alignment with holo pockets plays a critical and **bidirectional** role in our tri-modal contrastive framework, motivated by the following:\n\n1. **Holo → Cavity - Guidance for learning from noisy input**:\nHolo pockets, shaped by actual ligand binding, serve as **reliable supervisory anchors** (as stated by the reviewer). Aligning detected cavities with holo pockets helps the model extract meaningful features even from noisy or imprecise cavity detection.\n\n2. **Cavity → Holo - Addressing structural uncertainty and generalization**:\nHolo pockets are **biased toward a specific ligand series**(cf. Suppl. A.1-3), while other actives may bind elsewhere. By aligning cavities (which are ligand-agnostic) to holo pockets, the model learns features that are **spatially intrinsic to the protein**. This improves the model’s robustness to structural uncertainty and enhances generalization. This alignment also **boosts performance on holo structures**, since the learned features are less tied to ligand-specific conformations.\n\n3. **Cavity ↔ Holo - Dual modeling of general and specific signals**:\nThe model jointly learns **ligand-independent (intrinsic to proteins) and ligand-specific** pocket representations, which improves robustness. This enables **stronger performance across both holo and apo structures**, not just mitigating the performance gap but outperforming DrugCLIP in both cases.\n\n**In summary**, holo-cavity alignment is essential for modeling **both precise and generalizable binding patterns**, supporting our goal of robust virtual screening under structural uncertainty."}, {"review_id": "qHMIVh0CnA", "reviewer": "Reviewer_u1UJ", "summary": "This 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.", "questions": "Can the authors clarify whether any target-level or structure-level overlap exists between training and test sets?", "limitations": "None", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "[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.", "was_revised": true, "final_justification": "The authors have convincingly addressed the main concerns raised in the initial review.\n\nSpecifically, the addition of DEKOIS-based evaluation (Table 1) strengthens the evidence for generalizability beyond the original benchmarks. The explanation of why FlowDock is less suited for large-scale virtual screening, along with the new empirical comparison (Table 2), provides a clear justification for its exclusion as a primary baseline. The clarification on cavity detection and the demonstration of robustness across tools further support the model's design choices.\n\nGiven these clarifications and additional results, I am increasing my score.", "rebuttal": "> W1: 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\nWe thank the reviewer for pointing this out. Due to time constraints, we were unable to identify and verify the experimental apo structures corresponding to DEKOIS targets. However, we mapped the DEKOIS targets to UniProt ID and successfully downloaded 77 target structures from the AlphaFold Database, excluding 4 virus-related targets that are not available (HIV1PR & HIV1RT: P03366, SARS-HCoV: P0C6X7, NA: P27907).\n\nWe evaluated our method and baselines under three settings:\n\n* **Holo**: using the known holo pocket.\n\n* **Apo-pred (annotated)**: pocket detected near the ligand (used only for annotation).\n\n* **Apo-pred (blind)**: fully blind screening setting.\n\nAs shown in Table 1, our method consistently **outperforms DrugCLIP across all settings**, and **surpasses traditional docking baselines under holo pocket settings** (*italic*) when tested under **the most challenging apo-pred blind setup** (**bolded**).\n\n**Table 1.** Performance on DEKOIS (77 targets) under holo, apo-pred-annotated (the cavity detected by FPocket that is closest to the holo pocket), and apo-pred-blind settings.\n\n| Method    | AUROC (holo) ↑ | AUROC (apo-annot) | AUROC (apo-blind) | BEDROC (holo) ↑ | BEDROC (apo-annot) | BEDROC (apo-blind) | EF\\@0.5% (holo) ↑ | EF\\@0.5% (apo-annot) | EF\\@0.5% (apo-blind) |\n|-|-|-|-|-|-|-|-|-|-|\n| Vina | –  | –   | –    | –| –| – | 2.58 | – | –   |\n| GOLD | –  | –   | –    | –| –| – | 10.32| – | –   |\n| Glide     | –  | –   | –    | –| –| – | *11.85*| – | –   |\n| DrugCLIP  | 0.7967  | 0.7075  | 0.4355   | 0.532| 0.322 | 0.0313 | 20.62| 12.12  | 0.92|\n| AANet     | 0.8785  | 0.8624  | 0.7708   | 0.6446    | 0.5824| 0.3891 | 23.26| 20.91  | **13.33**    |\n\n\n> W2: 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\nWe thank the reviewer for this insightful comment. While our current framework employs Fpocket for cavity detection during training, we would like to clarify and justify this design with the following points:\n\n1. **Robustness across different cavity detectors**: Although our model uses Fpocket to extract training cavities, **the downstream performance remains stable across different cavity detection tools** (cf. Table 4). These conventional detectors are grounded in **physicochemical definitions of binding pockets**, such as surface concavity, void volume, and geometric constraints. The consistency across tools suggests that AANet is **not overfitting to a specific detection algorithm**, but instead learns **underlying structural priors** intrinsic to proteins, such as spatial enclosures or clefts.\n\n2. **Alignment with practical structure-based pipelines**: Our method follows the typical structure-based virtual screening pipeline, where **pocket detection and binding assessment are distinct stages**. This modularity aligns with standard workflows in drug discovery and allows our method to **plug into existing infrastructure** while remaining adaptable to alternative cavity detectors.\n\n3. **Physically meaningful supervision**: By grounding learning on cavities defined via physical heuristics (e.g., void detection), we introduce inductive bias from medicinal chemistry intuition into the model. This helps prevent overfitting to spurious binding patterns and encourages the model to focus on **biophysically** plausible pockets. **Concerns about physical plausibility have recently gained attention** in the structure prediction and docking communities, as highlighted by works such as PoseBusters in deep learning-based docking. This emphasis on biophysical realism is equally relevant to virtual screening and motivates the design of our method.\n\nAlthough we currently do not explore end-to-end cavity prediction due to time and resource constraints, we agree that **learning pocket detection in an end-to-end fashion is a promising direction**, especially on novel targets including flexible proteins. As datasets grow and modeling techniques evolve, we believe this integration could offer even more robust and generalizable solutions.\n\n> W3: Lack of comparison with recent methods like FlowDock and ProFSA.\n\nWhile we appreciate the reviewer’s suggestion regarding FlowDock and ProFSA, which enrich the diversity of baseline methods, we would like to clarify that **flexible docking frameworks like FlowDock are not primarily designed for large-scale virtual screening tasks**. Instead, they are better suited for **post-screening re-ranking or refinement stages**, where a small number of candidate actives are already identified. \n\nFlexible docking excels at capturing subtle conformational changes and fine-grained interactions between a specific ligand and a pocket, making it valuable for discriminating between a few similar binders. However, in large-scale virtual screening where the key objective is to enrich true actives from a vast set of mostly inactive molecules, such methods are **less effective when screening against millions of decoys**, as required in virtual screening settings. We will **include a discussion of flexible docking methods like FlowDock in the Related Work section** of the paper to provide a more complete picture in the context of apo setting.\n\nTo address the reviewer’s concern, we included results from DrugCLIP (ProFSA) using the checkpoint publicly released in the supplementary data of bioRxiv 2024.09.02.610777, which was fine-tuned from ProFSA with the DrugCLIP task. Interestingly, DrugCLIP (ProFSA) underperforms the original DrugCLIP baseline in our virtual screening benchmark. We hypothesize that this may be due to its pseudo-ligand-based pretraining, which may have enhanced the model’s ability to recognize general protein–ligand interaction patterns but also made it more prone to misinterpreting peptide-binding interfaces as small-molecule binding pockets.\n\nAlthough FlowDock’s computational cost prevented us from performing large-scale comparisons on the full dataset, we conducted a direct empirical evaluation on a representative subset. Specifically, we randomly sampled **10% of actives and 5% of decoys across 38 apo targets from DUD-E**, which may approximately **halve the number of Enrichment Factors (EF)** reported. For efficiency, we **generated only one ligand conformer per molecule** (instead of the default five), which **may degrade FlowDock’s performance**. This reduced the total number of complexes to < 30k and allowed us to complete all FlowDock runs within 3 GPU days. These results are shown in Table 2.\n\nIn contrast, our method directly targets binder enrichment under structural uncertainty and is optimized for efficiency, robustness, and early enrichment metrics. Nevertheless, AANet significantly outperforms FlowDock across all key metrics on the subset, as shown below. We also acknowledge FlowDock’s strengths in generating high-quality complex structures, which may provide additional interpretability or utility in downstream stages of the drug discovery pipeline.\n\n**Table 2.** Performance comparison on apo-blind virtual screening subset.\n\n| Method | AUROC ↑ | BEDROC (α=80.5) ↑ | EF@1% ↑ | EF@5% ↑ |\n|-|-|-|-|-|\n| DrugCLIP    | 0.6342 | 0.2567 | 9.04   | 4.58   |\n| DrugCLIP (ProFSA)| 0.6317 | 0.1799 | 5.71   | 3.83   |\n| FlowDock    | 0.5863 | 0.2358 | 8.15   | 5.24   |\n| AANet (Ours) | **0.8651** | **0.6729** | **24.38** | **12.12** |\n\n> Q1: Can the authors clarify whether any target-level or structure-level overlap exists between training and test sets?\n\nWe thank the reviewer for raising this important point.\nOn **PDBbind**, we removed duplicate complexes based on structure IDs, following prior work to ensure fair comparison. However, this may not fully eliminate target-level redundancy. For some of baselines, duplicates were not removed (cf. Supplementary Section B.7). On **ChEMBL**, we filtered data using UniProt IDs, which more reliably removes overlapping targets.\n\nTo further address potential leakage, we conducted an additional experiment by using MMseqs2 to **identify and remove any training proteins (from PDBbind and ChEMBL) containing chains with ≥90% sequence identity** to any DUD-E structure / target sequences. We then compared our method and DrugCLIP under both the original and this more stringent de-redundant / de-homologous setting. As shown in Table 3, our method maintains robust performance even after removing homologous sequences, demonstrating better generalization.\n\n**Table 3.** Performance on DUD-E under de-redundant and de-homologous (90%) settings.\n\n| Method | BEDROC (holo) | BEDROC (apo-pred annot) | BEDROC (apo-exp annot) | BEDROC (apo-pred blind) | BEDROC (apo-exp blind) | EF@1% (holo) | EF@1% (apo-pred annot) | EF@1% (apo-exp annot) | EF@1% (apo-pred blind) | EF@1% (apo-exp blind) |\n|-|-|-|-|-|-|-|-|-|-|-|\n| DrugCLIP (de-redundant)   | 0.5157   | 0.3746    | 0.3493  | 0.1974    | 0.1926  | 33.70  | 22.70  | 21.36     | 12.05  | 11.75     |\n| DrugCLIP (de-homologous)  | 0.3949   | 0.2323    | 0.2134  | 0.1097    | 0.1298  | 24.90  | 13.56  | 12.59     | 6.51   | 7.36 |\n| AANet (de-redundant) | **0.6365**   | **0.6003**    | **0.5866**  | **0.6232**    | **0.5764**  | **40.85**  | **38.46**  | **38.03**     | **40.85**  | **37.19**     |\n| AANet (de-homologous)     | *0.5985*   | *0.5247*    | *0.5475*  | *0.5955* | *0.5411*  | *38.55*  | *33.59*  | *34.76*     | *38.34*  | *34.50*     |"}, {"review_id": "vfLTkzLyYW", "reviewer": "Reviewer_TXmb", "summary": "The 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.", "questions": "1. 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", "limitations": "1. 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.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**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”", "was_revised": true, "final_justification": "The authors have clarified the key concerns I raised, especially regarding dataset preprocessing, generalization across homologs, and LBVS comparisons. Based on the clarifications and new results, I am raising my score.", "rebuttal": "> W1: 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.\n\nWe thank the reviewer for pointing this out. We will include the following details in the final version for clarity.\n\nThe ChEMBL35 dataset was processed as follows:\n\n**Molecule filtering**:\n \n* Removed salts or kept only the largest fragment\n* Kept molecules with molecular weight between [100, 800]\n* Removed those containing atoms other than [H, C, N, O, F, Cl, Br, I, S, P, B, Se]\n* Filtered out molecules with unbranched long chains containing 6 atoms or more.\n\n**Activity filtering**: We retained activity records with:\n\n* Confidence score = 9\n* Assay type = functional or binding\n* Standard type ∈ ['Ki', 'IC50', 'Kd', 'EC50', 'ED50', 'AC50', 'XC50']\n* Values converted to molar units and filtered to lie within the –log10 range [5, 12].\n\n**Target mapping**: Protein targets were mapped to UniProt IDs and matched with AlphaFold structures. Targets without available predicted structures were discarded.\n\n**Contrastive supervision masking**: Each known protein–ligand activity pair was recorded using its UniProt ID and InChI-key. During training, we masked non-diagonal entries that correspond to active (positive) pairs to ensure that true positive pairs are never treated as negatives, even if they are not aligned in the current batch. This masking strategy ensures correct supervision and avoids misleading the model during contrastive learning.\n\n> W2: 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\nWe thank the reviewer for the observation. We would like to clarify the following:\n\n**Marginal difference and metric interpretation**:\nThe performance gap is marginal (EF@1%: 5.51 vs 5.13), and our model **outperforms DrugCLIP in BEDROC** (0.0677 vs. 0.0623), which better reflects early enrichment — a critical criterion for practical screening.\n\n**Unconventional binding sites in LIT-PCBA**:\nLIT-PCBA contains atypical targets such as **mTORC1** and **PKM2**, where the ligand binds at **oligomeric interfaces** (hetero- or homo-dimers). Our method uses **monomeric AlphaFold structures** for second-stage training, which may reduce performance on these targets. Notably, our model **trained only on PDBbind still outperforms DrugCLIP on the full LIT-PCBA dataset** (EF@1%: 5.86 vs. 5.51; BEDROC: 0.0699 vs. 0.0623), suggesting the performance is not dependent on cavity augmentation. \n\n**Broader generalization**:\nOur method consistently outperforms DrugCLIP on a variety of datasets, including **DUD-E**, its curated subsets, **LIT-PCBA subsets**, and the newly added **DEKOIS benchmark**. This demonstrates strong generalization beyond any specific data augmentation strategy.\n\n**Table 1.** Performance on DEKOIS (77 targets) under holo, apo-pred-annotated (the cavity detected by FPocket that is closest to the holo pocket), and apo-pred-blind settings.\n\n| Method  | AUROC (holo) ↑ | AUROC (apo-annot) | AUROC (apo-blind) | BEDROC (holo) ↑ | BEDROC (apo-annot) | BEDROC (apo-blind) | EF\\@0.5% (holo) ↑ | EF\\@0.5% (apo-annot) | EF\\@0.5% (apo-blind) |\n|-|-|-|-|-|-|-|-|-|-|\n| Vina | –  | – | –  | –| –| – | 2.58 | – | – |\n| GOLD | –  | – | –  | –| –| – | 10.32| – | – |\n| Glide | –  | – | –  | –| –| – | *11.85*| – | – |\n| DrugCLIP  | 0.7967  | 0.7075  | 0.4355 | 0.532| 0.322 | 0.0313 | 20.62| 12.12  | 0.92|\n| AANet | 0.8785  | 0.8624  | 0.7708 | 0.6446  | 0.5824| 0.3891 | 23.26| 20.91  | **13.33**  |\n\n> Q1: Is there any discussion of ligand-based virtual screening methods (LBVS)? For example, traditional LBVS methods (SHAFTS) and recent deep learning-based approaches (S-MolSearch) can bypass the need for protein structural information entirely.\n\nWe thank the reviewer for highlighting the relevance of ligand-based virtual screening (LBVS), which can indeed bypass the need for protein structural information and is particularly useful when high-quality ligands are available or when protein structures are unreliable.\n\nWe would like to clarify that **LBVS and SBVS address different use cases**. LBVS is often more straightforward when good query ligands are available (e.g., potent, exogenous ligands), but may be less effective in identifying novel chemotypes or in the absence of relevant prior ligands. In contrast, SBVS leverages the protein’s structural context, which can help discover structurally diverse actives but depends on structure quality.\n\nRegarding the methods mentioned:\n\n* SHAFTS is relatively outdated and unfortunately could not be executed on our current hardware.\n\n* S-MolSearch does not provide pre-trained checkpoints, training data, or inference code, making reproduction infeasible at this time.\n\nHowever, to address the reviewer’s concern, we **evaluated several widely-used LBVS methods**, covering both 2D and 3D paradigms, including **ECFP** (2D topology-based), **USR** (3D shape-based), and **Shape** from Schrödinger suite (3D shape overlap). The results are summarized in Table 2.\n\n**Table 2.** Performance of common LBVS methods on DUD-E benchmark\n\n| Method | EF@1% | BEDROC (α=80.5) |\n|-|-|-|\n| USR [1]  | 2.74  | 0.051 |\n| PhaseShape [2] | 8.29  | 0.138 |\n| ECFP4 [3] | 26.61 | 0.414 |\n| AANet (apo-blind) | 37.19 | 0.576 |\n\n[1] J Comput Chem. 2007, 28, 10, 1711-23.\n[2] J. Chem. Inf. Model. 2011, 51, 10, 2455–2466.\n[3] J. Chem. Inf. Model. 2010, 50, 5, 742–754.\n\nThe notably strong performance of ECFP4 is likely due to the fact that many actives in virtual screening benchmarks such as DUD-E share similar topological scaffolds, whereas decoys are specifically designed to have similar physicochemical properties but divergent topologies. As a result, ECFP4 can effectively distinguish actives from decoys based on topological similarity alone, leading to higher enrichment factors. However, such advantages may not always generalize to real-world screening scenarios, where novel actives are often needed.\n\n> Q2: Is removing overlapping UniProt IDs sufficient to prevent information leakage between training and test sets? How do the authors ensure that models are not indirectly exposed to structurally or functionally similar targets?\n> L3: The data split removes overlapping UniProt IDs but does not filter by sequence similarity.\n\nWe thank the reviewer for raising this important concern regarding potential overlap between training and test data.\n\nTo further rule out information leakage, we performed an additional experiment using **MMseqs2 to remove any proteins in our training sets (PDBbind and ChEMBL) that contain chains sharing ≥90% sequence identity** with any DUD-E target. We then compared performance under both the original de-redundant setting and this stricter de-homologous setting. As shown in Table 3, **our method exhibits only a minor drop in performance**, maintaining strong results across all settings. This demonstrates the robust generalization of our approach, even in the absence of closely related training proteins.\n\n**Table 3.** Performance on DUD-E under de-redundant and de-homologous (90%) settings.\n\n| Method | BEDROC (holo) | BEDROC (apo-pred annot) | BEDROC (apo-exp annot) | BEDROC (apo-pred blind) | BEDROC (apo-exp blind) | EF@1% (holo) | EF@1% (apo-pred annot) | EF@1% (apo-exp annot) | EF@1% (apo-pred blind) | EF@1% (apo-exp blind) |\n|-|-|-|-|-|-|-|-|-|-|-|\n| DrugCLIP (de-redundant)   | 0.5157   | 0.3746    | 0.3493  | 0.1974    | 0.1926  | 33.70  | 22.70  | 21.36     | 12.05  | 11.75     |\n| DrugCLIP (de-homologous)  | 0.3949   | 0.2323    | 0.2134  | 0.1097    | 0.1298  | 24.90  | 13.56  | 12.59     | 6.51   | 7.36 |\n| AANet (de-redundant) | **0.6365**   | **0.6003**    | **0.5866**  | **0.6232**    | **0.5764**  | **40.85**  | **38.46**  | **38.03**     | **40.85**  | **37.19**     |\n| AANet (de-homologous)     | *0.5985*   | *0.5247*    | *0.5475*  | *0.5955* | *0.5411*  | *38.55*  | *33.59*  | *34.76*     | *38.34*  | *34.50*     |\n\n> L1: 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.\n\nWe thank the reviewer for raising this important point.\n\n1. **Robustness across cavity detection tools:**\nOur method does **not rely on any specific cavity detection tool**. As shown in Table 4, AANet demonstrates **consistent performance across different pocket detection methods**.\n\n2. **Limitation in flexible or disordered proteins**:\nWe acknowledge that for highly flexible or intrinsically disordered proteins, where well-formed pockets are absent and ligand-induced fit is essential, **existing pocket-based SBVS methods, including ours, face inherent challenges**. Our work primarily addresses uncertainty in binding site localization, not full flexible binding modeling.\n\nWe agree this is a critical direction, and we consider **end-to-end pocket prediction and flexible modeling** promising avenues for future research.\n\n> L2: No confidence or uncertainty estimation in predictions.\n\nWe appreciate the reviewer’s insightful comment and agree that quantifying prediction confidence would enhance the method’s practical value. While AANet does not currently produce explicit uncertainty estimates, its design of modeling interactions with both binding and non-binding cavities naturally provides multiple levels of structural signal that could support future confidence modeling. Leveraging this structure-aware framework for uncertainty estimation is one of our priority directions for future work."}, {"review_id": "JBAoYkMHKo", "reviewer": "Reviewer_x4Dm", "summary": "The 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.", "questions": "1. 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.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "The 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.", "was_revised": true, "final_justification": "I believe that the paper would be a good fit for the conference, as it shows a well-executed idea and the authors demonstrate consistent results throughout their benchnmarks. Also reviewing the author responses, it seems like the reviewers share a similar opinion that the manuscript could be good fit for the conference.\n\nDuring the rebuttal, the authors have run new experiments and provided additional results that will further strengthen the manuscript. \n\nAs a core weakness, I listed the missing standard deviations and error bars. During the rebuttal, the authors have provided some more additional results, but I think this should have been done for all experiments. Still, as the works they benchmark against do not provide any error bars either, I think it is okay in the current form. \n\nAnother important aspect for me is the discussion about structure similarity in the benchmark. They have fully addressed this during the rebuttal and would like that they include this table in the revised manuscript.\n\nOverall, I believe that this is a high-quality paper suitable for this conference.", "rebuttal": "> W1: No standard deviations or error bars.\n\nWe did not include standard deviations initially because most prior studies do not report results or offer pre-trained models from multiple runs, making direct comparison of variability infeasible. However, to address this concern, we have conducted four parallel runs of our method on the DUD-E benchmark to estimate variability. The resulting standard deviations are now included in Table 1.\n\n**Table 1.** Performance (mean ± std) on the DUD-E benchmark.\n\n| AANet  | holo  | apo-exp-annotated | apo-pred-annotated | apo-exp-blind  | apo-pred-blind |\n| - | - | - | - | - | - |\n| BEDROC | 0.6324±0.0074 | 0.5737±0.0088 | 0.5912±0.0061  | 0.5706±0.0061 | 0.6099±0.0090 |\n| EF@1%  | 40.73±0.51 | 37.12±0.57 | 37.59±0.58  | 37.00±0.54 | 39.91±0.68 |\n\nWe hope this addition improves the clarity and reproducibility of our reported results.\n\n> W2: Broader method comparison and related work discussion.\n\nWe thank the reviewer for the suggestion. Since we evaluate our model on newly curated benchmark settings, all baselines had to be **re-implemented or re-run**, which limited the scope of direct comparisons. Some previously published methods used in related studies either lack publicly available inference code or are outdated. We include this comparison with previously reported results in Supplementary Section C.2.\n\nWe have further expanded our comparisons to include the following two categories:\n\n* Several representative ligand-based virtual screening (LBVS) methods (ECFP, USR, PhaseShape from the Schrödinger suite):\n\n| Method   | EF@1% | BEDROC (α=80.5) |\n| - | - | - |\n| USR [1]  | 2.74  | 0.051   |\n| PhaseShape [2] | 8.29  | 0.138   |\n| ECFP4 [3]   | 26.61 | 0.414   |\n| AANet (apo-blind) | 37.19 | 0.576   |\n\n[1] J Comput Chem. 2007, 28, 10, 1711-23.\n[2] J. Chem. Inf. Model. 2011, 51, 10, 2455–2466.\n[3] J. Chem. Inf. Model. 2010, 50, 5, 742–754.\n\n* A flexible docking method (FlowDock) based on flow matching.\n\n| Method | AUROC ↑ | BEDROC (α=80.5) ↑ | EF@1% ↑ | EF@5% ↑ |\n| - | - | - | - | - |\n| FlowDock [1] | 0.5863   | 0.2358  | 8.15  | 5.24  |\n| AANet  | 0.8651   | 0.6729  | 24.38 | 12.12 |\n\n[1] FlowDock: Geometric Flow Matching for Generative Protein-Ligand Docking and Affinity Prediction, ISMB 2025.\n\nDue to computational constraints, FlowDock was evaluated on a subset of DUD-E compounds, which may underestimate its EF values (roughly halved). We acknowledge that **flexible docking frameworks like FlowDock are not primarily designed for large-scale virtual screening tasks**. Instead, they are better suited for **post-screening re-ranking or refinement stages**, where a small number of candidate actives are already identified. We will further enrich the related work section to reflect these comparisons and position our method more clearly in the context of existing approaches.\n\n> Q1: Pocket selection consistency during training?\n\nWe thank the reviewer for this question. During training, we **randomly sample one candidate cavity on the fly** from either the set of positive (overlapping with holo pocket) or negative (non-overlapping) Fpocket-detected pockets on the same protein structure. If no positive pocket is available, a negative one is used instead.\n\nImportantly, all sampling is controlled by a random seed, ensuring reproducibility: for a given dataset, seed, and training setup, the same pocket will be selected for the same sample at the same epoch across different runs. This design balances stochastic training dynamics with full reproducibility.\n\n> Q2: Could the method benefit from more augmentations (e.g., randomized positions, alternative predicted structures)?\n\nWe thank the reviewer for this valuable suggestion. Due to computational constraints, we were unable to explore such augmentations in this work. However, we agree that these strategies are promising, especially for future engineering-scale models.\n\n1. **Disturbed pocket positions** may serve as an effective regularization strategy. However, fully random centers could undermine the **spatial rationale defined by pocket detection tools**, which often capture protein-intrinsic cavity features.\n2. Using **diverse predicted structures** is a highly promising augmentation. Recent studies (e.g., shallow MSA-based predictions [1], AF-Cluster [2]) show that AlphaFold can generate **alternative conformations representing distinct functional states** (e.g., DFG-in/out in kinases; transporters and G-protein-coupled receptors). Such sampling could help the model learn flexible pocket features. Similarly, including structures from alternatives such as ESMFold or Boltz1 may improve generalization by exposing the model to **distributional variability** in predicted structures.\n\nWe believe these augmentations are valuable actions for improving robustness under structural uncertainty.\n\n[1] Elife, 2022, 11, e75751.\n[2] Nature, 2024, 625(7996), 832–839.\n\n> Q3: Clarification on training with pocket-agnostic data: staged vs. joint optimization? How is λ chosen?\n\nWe thank the reviewer for pointing this out and will improve the clarity in the final version.\n\nOur training follows a **two-stage design**:\n\n1. **Stage 1 (pocket-aware)**: We train a dual-tower model with contrastive loss on paired pocket–ligand data.\n2. **Stage 2 (pocket-agnostic)**: We freeze the backbone encoders and introduce lightweight adapters—a cross-attention adapter on the pocket side (approximating an argmax match) and an identity-like linear adapter on the ligand side. This preserves the model’s ability to identify binding pockets from the original contrastive alignment, while enabling it to effectively leverage pocket-agnostic data for further learning.\n\nThis design allows the model to reuse DrugCLIP-style embeddings and enables efficient inference while enhancing learning from broader activity/binding datasets.\n\nAs for the **λ setting**, we use a simple value of **1.0**. At this scale, the contrastive loss is naturally two orders of magnitude larger than the KL loss, providing sufficient regularization without dominating training. A larger λ overly constrains the model (resulting in little learning), while a smaller λ reduces its regularizing effect.\n\n> Q4: Did the authors account for structural similarity when removing overlapping entities with DUD-E or LIT-PCBA to prevent data leakage?\n\nWe thank the reviewer for this important question. We acknowledge that **ID-based filtering does not eliminate structurally similar entries**. We followed this approach primarily to **align with prior work and ensure fair comparisons**. Notably, some baselines did not apply such filtering at all (cf. Supplementary Section B.7).\n\nTo further mitigate potential leakage, we performed an additional analysis using MMseqs2 to identify and exclude any training proteins (from PDBbind and ChEMBL) that contain chains with **≥90% sequence identity** to any DUD-E structure or target sequence. We then compared our method and DrugCLIP under both the original de-redundant and this **stricter de-homologous setting**.\nAs shown in Table 4, our method maintains strong performance even after removing homologous sequences, demonstrating superior generalization.\n\n**Table 4.** Performance on DUD-E under de-redundant and de-homologous (90%) settings.\n\n| Method | BEDROC (holo) | BEDROC (apo-pred annot) | BEDROC (apo-exp annot) | BEDROC (apo-pred blind) | BEDROC (apo-exp blind) | EF@1% (holo) | EF@1% (apo-pred annot) | EF@1% (apo-exp annot) | EF@1% (apo-pred blind) | EF@1% (apo-exp blind) |\n| - | - | - | - | - | - | - | - | - | - | - |\n| DrugCLIP (de-redundant)  | 0.5157  | 0.3746   | 0.3493  | 0.1974   | 0.1926  | 33.70  | 22.70   | 21.36  | 12.05   | 11.75  |\n| DrugCLIP (de-homologous) | 0.3949  | 0.2323   | 0.2134  | 0.1097   | 0.1298  | 24.90  | 13.56   | 12.59  | 6.51 | 7.36   |\n| AANet (de-redundant)  | **0.6365** | **0.6003**  | **0.5866** | **0.6232**  | **0.5764** | **40.85** | **38.46**  | **38.03** | **40.85**  | **37.19** |\n| AANet (de-homologous) | *0.5985* | *0.5247*  | *0.5475* | *0.5955*  | *0.5411* | *38.55* | *33.59*  | *34.76* | *38.34*  | *34.50* |\n\n> Q5: Could training a model on pockets randomly sampled from multiple detection methods improve generalization?\n\nWe thank the reviewer for this suggestion. We investigated this possibility and found the following:\n\n1. The **slight performance drop** observed with certain pocket detection tools largely stems from **individual tool failures on specific targets**, especially on AF2-predicted structures. For example, PocketFinder failed to detect the correct pocket for IGF1R, PTPN1, and RENI (IoU < 0.1); SurfNet failed on EGFR, IGF1R, RENI, and RXRA. In contrast, Fpocket consistently detected valid pockets, making it more robust overall.\n2. In early-stage experiments (Stage 1 model only, without adapters), we augmented training data using **positive pockets from three other detection tools**. This yielded no consistent improvement: for example, EF@1% on the AF2-annotated setting dropped slightly (34.4 → 33.7), while AF2-blind improved modestly (29.3 → 30.7). Similar minor improvements were observed across apo settings. Given AANet’s demonstrated robustness across tools, we believe the model has already internalized spatial priors shared across detection algorithms, reducing the benefit of this augmentation strategy."}]

Author rebuttal:

Meta-review: This paper focuses on the apo-structure protein binding with ligand for virtual screening task. The main contribution of the paper is that it proposes a tri-modal contrastive learning method and the a cross-attention based adapter training. Though advanced experiments, the method shows improvement on the early enrichment factory by a large margin. 

Reviewers have give positive feedback to the work, on the novelty of the method and the reasonable setting and clear motivation. The results are also well acknowledged by the reviewers. Though some concerns have raised, most of them have been addressed and the reviewers are mostly satisfied of the rebuttal.
The reviewers are encouraged to add more discussion as responded in the rebuttal.
Decision: Accept (poster)