Test-Time Adaptation without Source Data for Out-of-Domain Bioactivity Prediction
Authors: Yiming Yang
Zhiyuan Zhou
Yueming Yin
Hoi-Yeung Li
Adams Wai-Kin Kong
Venue: ICLR 2026 Poster (2026)
Keywords: out-of-domain bioactivity prediction
source data-absent
test-time adaptation
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: We explore a realistic bioactivity prediction setting, where models adapt to out-of-domain distributions without source data, leveraging test-time adaptation for robust bioactivity prediction.

Abstract: Accurate prediction of protein-ligand bioactivity is a cornerstone of modern drug discovery, yet current deep learning methods often struggle with out-of-domain (OOD) generalization. The existing methods rely on access to source data, making them impractical in scenarios where data cannot be accessed due to confidentiality, privacy concerns or intellectual property restrictions. In this paper, we provide the first exploration of a more realistic setting for bioactivity prediction, where models are expected to adapt to out-of-domain distributions without access to source data. Motivated by the critical role of binding-relevant interactions in determining ligand-protein bioactivity, we introduce an uncertainty-weighted consistency strategy, in which original samples with high confidence guide their augmented counterparts by minimizing feature distance. This encourages the model to focus on informative interaction regions while suppressing reliance on spurious or non-causal substructures. To further enhance representation discriminability and prevent feature collapse, we integrate a contrastive optimization objective that pulls together augmented views of the same complex and pushes away views from different complexes. Together, these two components enable the learning of invariant, bioactivity-aware representations, allowing robust adaptation under distribution shifts. Extensive experiments across DTIGN, SIU 0.6, and DrugOOD demonstrate that our framework consistently outperforms state-of-the-art baselines under scaffold, protein, and assay based OOD settings. Especially on the eight subsets of DTIGN, it improves Pearson’s $R$ by 8.2\% and Kendall’s Tau $\tau$ by 5.8\% on average over the best baseline, underscoring its effectiveness as a source data-absent solution for OOD bioactivity prediction.

Published as a conference paper at ICLR 2026
TEST-TIME ADAPTATION WITHOUT SOURCE DATA FOR OUT-OF-DOMANI BIOACTIVITY PREDICTION
Yiming Yang, Zhiyuan Zhou; Yueming Yin,
Hoi-Yeung Li!
Adams Wai-Kin Kong*
Nanyang Technological University miming014 zhouC457) (yueming. yin, hyli, i, www.tuuuuuuuuueee... sg,
ABSTRACT
Accurate prediction of protein-ligand bioactivity is a cornerstone of modern drug discovery, yet current deep learning methods often struggle with out-of-domain (OOD) generalization. The existing solutions rely on access to source data, mak- ing them impractical in scenarios where data cannot be accessed due to confi- dentiality, privacy concerns or intellectual property restrictions. In this paper, we provide the first exploration where models are expected to adapt to distribution shifts without access source data. Motivated by the critical role of binding-relevant interactions in determin- ing ligand-protein bioactivity, strategy, in which original samples with high confidence guide their augmented counterparts by minimizing feature distance. This encourages the model fo- cus on informative interaction regions while suppressing reliance on spurious or non-causal substructures. To further enhance representation discriminability and prevent feature collapse, we integrate a contrastive optimization objective that pulls together augmented views of the same complex and pushes away views from different complexes. Together, these two components enable the learning invariant, bioactivity-aware representations, allowing robust adaptation under distribution shifts. Extensive experiments across DTIGN, SIU 0.6, and DrugOOD demonstrate that our framework consistently outperforms state-of-the-art base- lines under scaffold, protein, and assay based OOD settings. Especially the eight subsets of DTIGN, it improves Pearson's 's R by 8.2% and Kendall's Tau r by 5.8% on average the best baseline, underscoring its effectiveness source data-absent solution for OOD bioactivity prediction.
a more realistic setting for bioactivity prediction,
introduce an uncertainty-weiihee consistency
INTRODUCTION
The traditional drug discovery process is limited by lengthy operation cycles and enormous eco- nomic costs, with the development of single new drug typically requiring nearly decade and about $3 billion investment (Hughes et 2011 Ejalonibu et al. 2021). Recent advances in deep learning have spurred growing interest in Al-drivn drug discovery, enabling considerable break- throughs in diverse areas such as molecular property prediction (Zitnick et al.) 2022; Ying et al.] 2021) protein folding (Jumper et al. 2021 Lin et al. 2023), and protein-ligame interaction mod- eling (Kong et al.) 2024] Wang et al. 2023). These methods aim to accelerate the identification of promising ligands and elucidate complex biological mechanisms, thereby reducing the reliance manual processes. In this work, we focus on the problem of bioactivity prediction, which seeks to identify ligand molecules capable of modulating the functional outcomes of target proteins.
With the increasing adoption of pocket-ligand modeling (Yin et al.) 2024 Huang et al. 2025 graph neural networks (GNNs) have become particularly prominent (Zhang et al. [023 Wang al., 2024b Mastropietro et al. 2023 Li et al. 2021), as graph-based representations offer natural and powerful means characterize molecular structures and interactions. Representative models such as DTIGN Yin et al. 2024 and AttentionMGTT-DT. (Wu et al. 2024a) exhibit impressive capabilities
Equal contribution.
Corresponding author.

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Published as a conference paper at ICLR 2026
across variety of tasks (e.g., ICsu, ECsu, Ka, and Kj). Despite their potential, existing methods
largely rely on the underlying hypothesis that training and testing data are independently sampled
from an identical domain (Kao al. 202I] Yuan et al. 2022 Wu et al. 2024a Yang et al. 2023).
In practice, however, real-world domains are often dynamic and uncertain, characterized by varying experimental conditions, novel molecular scaffolds, and previously unseen proteins (Ji et al.
2023
Huang et al. (025), which require the model to effectively handle distribution shifts (Ji et al. 2023
Yin et al. 2021). Even worse, target domains usually have limited labeled data from experiments for training and fine- fine-tuning. For instance, the COVID-19 pandemic highlights how an unpredictable event can introduce entirely new target proteins from an unknown distribution (Platto et al.) 2020),
underscoring the urgent need to enhance current methods with respect to OOD generalization.
Although approaches based on invariant learning (Sagawa et al. 2019 Wu al. 2024b Wang et al.) 2024a) and graph-based generalization (Wu et al. 2022] Zhu et al. (021) | could be applied to miti- gate the problem, they still face key limitation in real applications: the requirement for full access to source data, which may not be feasible due to confidentiality, privacy concerns or intellectual property restrictions, To this end, we introduce realistic but challenging source data-absent bioac- tivity prediction setting with only well-trained source model provided supervision. To address this challenging OOD setting for bioactivity prediction, we propose TAB a Test-time Adaptation framework for out-of-domain Bioactivity Prediction, which jointly leverages uncerfainty-weighted consistency learning and contrastive learning for effective source data-absent adaptation.
Our motivation for TAB is two-fold, illustrated in Figure [ On the one hand, bioactivity is inher- ently determined by specific biological interactions within the pocket-ligand complex. A ligand cannot function independently, while its activity critically de- pends on both the target protein and its surround- ing space. In this context, geometric features play central role in representing binding status and have been proven highly effective in modeling molecular interactions (Wei et al. [2024] Han et al. 2024 Song jet al., 2024, Zhang et al.] 2022b). In particular, E(3)- equivariant GNNs capture local spatial relationships on protein surfaces and significantly improve binding- site prediction (Wei et al., 20224.. As such, we aim to direct the model's attention toward the binding re- gion and the spatial arrangement between interacting molecules. On the other hand, bioactivity prediction models are susceptible to privileged substructure bias, where certain ligand moieties or recurrent protein mo- tifs frequently occur in active complexes but are neither causal determinants of binding nor bioactiv- ity. Such spurious correlations can be exploited as shortcut signals, leading to overfitting and poor generalization. For example, kinase inhibitor and GPCR ligand datasets contain many active ligands with methyl-substituted phenyl rings (Klekota & Roth 2008 Schneider & Schneider] 2017); like- wise, proteins may exhibit recurring surface patterns correlated with activity but not mechanistically responsible for binding (Johansson al. 2013). In both cases, models risk attributing activity to non-causal substructures, thereby limiting their robustness across domains.
Bioactivity depends on binding region and geometric cues
Shortcut structure
Real bioactivity- related region
Peripheral atam
masked atom
Previous models may rely on structure sharfcut and peripheral atoms
Direct the model's attention real binding regions instead of shortcut structures by masking
Figure 1: Motivation of TAB. The protein and ligand are shown in purple and brown, respectively.
Building on these insights, we first introduce the consistency component, where the augmented sam- ples are guided to align with their original confident counterparts, thereby discouraging reliance on spurious shorteuts and promoting bioaetivity-relevant invariant representations. This design natu- rally guides the model's attention toward informative binding regions, where geometric cues and interaction features are most critical for bioactivity (Zhou al. 2025). In addition, account for varying levels of prediction reliability across samples, we incorporate an uncertainty-weighe reg- ularization that prioritizes more confident predictions and stabilizes adaptation under challenging OOD conditions, as shown in Figure 2
Beyond consistency learning, we integrate contrastive component to enhance representation dis- criminability and prevent feature collapse. By encouraging alignment between augmented views of the same complex while separating representations of different complexes, this design reinforces binding-relevant relevant signals and sharpens the distinction between -relatd and irrelevant features.
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Combined with consistency learning, it yields complementary benefits that enable robust general- ization across diverse OOD settings under realistic, source data-absent conditions.
In summary, contributions can be listed follows:
To the best of our knowledge. no existing work has studied bioactivity prediction under OOD settings without access to source data. In this work, we introduce a realistic, yet challenging source data-absent bioactivity prediction setting in which only well-trained source model is available for supervision.
Inspired by the importance of binding- rellvattt interactions and the need to avoid spurious short- cuts, we propose self-supervised framework that integrates uncertainty-weighted consistency learning with contrastive optimization. This design directs the model's 's attention to informative pocket-ligand interactions, suppresses reliance on false privileged substructures, and promotes the learning of invariant, bioactivity-awarr representations.
Through extensive experiments on the DTIGN, SIU 0.6, and DrugOOD benchmarks, we demon- strate that TAB achieves oftte-of-the-art performance under scaffold, protein, and assay based OOD settings, delivering improvements in both predictive accuracy and ranking consistency. On the eight subsets of DTIGN, TAB improves Pearson's 's R by 8.2% and Kendall's Tau 7 by 5.8% on average over the best baseline, underscoring its effectiveness as a source data-absent solution for OOD bioactivity prediction.
RELATED WORK
Bioactivity Prediction. Accurately predicting the bioactivity of ligands against proteins represents a pivotal step in drug discovery. Early methods such as DeepDTA (Öztürk et al. 2018) and MT- DTI (Shin al., 2019) operate directly ligand SMILES strings and protein sequences using CNN or attention-based encoders. Subsequently, GraphDTA (Nguyen et al. 2021) represents drugs as molecular graphs, which enables the model directly capture bonds among atoms. Furthermore, GIGN (Yang et al., 2023) and DTIGN (Yin et al., (0244) construct geometric interaction graphs that integrate both ligand and protein information, allowing the model to better capture complex binding patterns. By progressively incorporating structural information, this class of methods pro- vides a more faithful representation of molecular interactions and thus has become the predominant paradigm in recent bioactivity prediction research.
Out-of-Domain Generalization in Bioactivity Prediction. The OOD issue poses critical chal- lenge in bioactivity prediction. Previous methods in alleviating this problem can be roughly cate- gorized into three groups: invariant learning, data augmentation, and graph-based approaches. Ap- proaches in the first group (Sagawa al.) 2019 Yin et al. 2021 2022 Wang et al. 2024a Wu et al.) 2024b) aim to capture the invariant patterns across different traminng environments. Data augmenta- tion (Wang al. 2021) is able to help OOD generalization it can enrich the training distribution. The last group methods (Wu et al. 2022] Zhu et al.) (02T) tackle this problem by exploiting graph structural information and identifying subgraphs that remain predictive across different domains. However, they all require full access to source data, either to construct multiple training environ- ments for learning invariance, generate augmented samples from the original distribution, to analyze graph structures in search of transferable subgraphs. In contrast, we propose to mitigate OOD effects test time address the demand from real applications, where source data is not available. This setting has never been studied according to the best of our knowledge.
Test-Time Adaptation in CV. Test-Time Adaptation (TTA) has been widely studied in conventional Computer Vision (CV) tasks for improving robustness. Approaches include self seff-supervised tasks such as rotation prediction (TTT (Sun et al. 20200) and masked image generation (TTT-MAE (Gan- delsman et al. 2022)), well as pseudo-labeling methods like SHOT (Liang et al. 2020) and MEMO (Zhagg et al. 2022a). Despite these advances, their potential for bioactivity prediction remains unexplored, where molecular data pose unique challenges due to structural complexity, in- herent substructure biases, and binding region modeling (Johansson et al.) 2013 Han et al. 2024). Motivated by this gap, we develop the first TTA method tailored to bioactivity prediction.
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Pre-trained encoder
All test data
Consistency
Init
Uncertointy-weighted module
learning
Encoder
/9
Augmented complex T(x)
Shored
f2
W Confidence score
Encoder
Contrastive
learning
Original complex
g0
fi
fi
Momentum encoder
1
f3
Augmented complex T(4)'
Pocket Atom
Ligand
Masked
Pull
Push
Figure 2: The framework of TAB.
3 METHOD
3.1 PROBLEM FORMULATION
We consider the task of source data-absent bioactivity prediction under domain shifts. Each input sample x E G is a pocket-ligand complex graph represented as x = (V, E), where V is the set of nodes corresponding atoms, and E is the of edges representing chemical bonds. Each node encodes atomic and chemical features, while edges capture both covalent and non-covalent bonds.
In this setting, only pretrained source model is available supervision, and direct access the source dataset is prohibited, including raw samples, intermediate features, and statistics. At test time, we assume access to only the unlabeled target samples Drest ((=:)) Nuest Iz E G. Specifically, the model parameters are updated by minimizing self-supervised objective Last. Formally, the adaptation process can be written as:
min EpwD (fo()))
(1)
where denotes the prediction model with parameters 0. The comparisons between our setting and existing adaptation settings in the bio dio-domain are summarized in Table [ While existing settings
employed in previous bio-related studies (Li al.) 2023] Moon al., 2023) He al. [022] Zeng
et al. 2024 Liu et al. 2023) serve for specific purposes, they fail to fully cover all practical scenarios where source data, target data, or supervision are not simultaneously available.
Table I: Characteristics of problem settings previous bio-related studies for adapting trained model to a potentially shifted test domain. Among the source and target data (x x²) and labels (y', y²), our test-time setting only requires access to the target data x'.
Setting
Source data
Target data
Training loss
Testing loss
Fine tuning eL al. 2023 Moon et al. 2023
X
C(e..
Continual learning He el nl. 2022
L(nt.g")
Domain adaptation
Zeng
2024
8
C(x v²) Lis" .xf)
Test-time adaptation (ours)
((2*)
3.2 UNCERTAINTY- WFIGHTTED CONSISTENCY LEARNING
Taking inspiration from the critical role of binding-relevant interactions, adopt consistency objective to encourage the model to focus on informative pocket-ligand regions while suppressing reliance spurious privileged substructures. The key idea is align the representations of each pocket-ligand complex with those of its perturbed counterpart, thereby emphasizing bioactivity- relevant invariant features, depicted in the upper part of Figure 2
Formally, let fo = fo;); and f. (Tx:)) denote the feature representations of the original and augmented samples, respectively, where T(-) denotes the perturbation process. For each pocket-
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Algorithm 1: TAB framework
Input: target dataset Drest. online encoder fe. momentum encoder for memory queue Q. momentum number of Monte Carlo dropout passes K, temperature Tex consistency weight a, contrastive weight |
Output: Labels of samples in target dataset Drest
1 Function UncerWeight, (x"):
14
# no gradient on fa
2
for k = 1 to do
15
Lar + efx f /Ta]/
3
(()) - fa(x")
( Ex=ref((fy ff. /Te) Σ exp(12 f//Te)l
4
+ R Lk= f(k)
16
return Let
5
o² + -- > ||p(k) nll2
17
Main procedure:
6
w w+ 1/(0² +e)
18
foreach I E Diest do
19
x² t x T(x)
7
return w
20
w + Uncerreeigtt (x")
8 Function ConsLoss (x" x² w):
21
Lcons t ConsLoss (x x², w) x= T(x)
9
f + f=(xx)) fa + fo cos cos f {()]
22
Lcons t EB(l
11
return Lcons
23
Latr t ContrLoss (x ar"," x 2c)e) fist t a -eens
Function ContrLoss (x",,x ,=" QeTe):
24
Lat
25
Update 0 by gradient descent using La
13
f + fe(r"), pa' + (x")
26
0' t m 0' + (1 (1- m)
ligand complex graph, randomly mask subset of atom and edge features. These augmentations tend to obscure false privileged substructures, thereby suppressing reliance on spurious shortcuts and encouraging the extraction of bioactivity-relevat invariant representations. Since the interface accounts for only a small portion of the entire complex, the majority of the masked content corre- sponds atoms and edges outside the binding site, which turn naturally directs attention toward the binding region. Even if some key atoms and edges are temporarily occluded, essential geometric cues and binding poses remain largely intact. We then enforce consistency by minimizing the co- sine distance between f/ and f,, encouraging the model to suppress reliance on spurious privileged substructures while maintaining sensitivity to binding-relevant interactions.
Moreover, we note that not all samples are equally reliable. To reduce the influence of noisy or uncertain predictions, we introduce an uncertainty-wett weighttd regularization scheme. Specifically, estimate confidence via Monte Carlo dropout: performing K stochastic forward passes with the original source model on the original input yields multiple feature samples f(a) (ax)), from which the mean μn and variance of are computed as:
K K
K
(I.).
of
1lf (R) (2.) - -11122
(2)
K
1=1
k=1
Accordingly, the confidence score for given sample is defined w; 1/(o +), with serving as a stabilizing constant. Thus, samples with higher confidence are assigned heavier weights during training, enabling the model to prioritize trustworthy information while mitigating the risk of error propagation. Finally, the overall consistency training objective is defined as:
Leons
1
Σ
B
(1
f [
(3)
B
1=1
where B is the mini-batch size.
3.3 JOINT CONTRASTIVE LEARNING
While consistency learning guides the model to focus on rendint regions, does not ex- plicitly encourage discrimination between complexes. To address this, integrate contrastive objective that promotes feature separability. Following the shared instance-discrimiiation princi- ple (Chen et al. 2020 He et al. 2020), treat features of two augmented views of the same

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pocket-ligand complex positive pairs, while features from different complexes serve negative pairs. Specifically, given a target input z, we reuse the augmented view ((x) already employed in the consistency objective, and generate bottom panel of Figure 2
additional augmented view T(x). , as shown in the This design ensures that the consistency-guidees view reinforces align- ment around binding-relevant signals, while the second augmentation introduces complementary variations that enrich the representation.
To increase the diversity of negative samples, we maintain a memory queue Q, which stores feature representations from previous mini-batches. The update is done by enqueue and dequeue in a FIFO manner. To stabilize the feature space and prevent rapid fluctuations, we adopt MoCo-style (He et al. 2020) momentum encoder for (-), whose parameters l' are initialized from the pretrained source weights and updated via exponential moving average:
0' + m6' + (1 m))0
(4)
where m E 10, is the momentum coefficient and 9 denotes the parameters of the online en- coder. This momentum update allows the encoder to provide stable feature representations for con- trastive learning without relying on backpropagation for every mini-batch. Specifically, we encode the consistency-guidee augmentation T(x) and the additional augmentation Tax) into embeddings f = fo (x) and fa fo (T' (x)), with the momentum embedding fa enqueued into Q. The contrastive loss encourages the online embedding f" be close to its positive counterpart f" while pushing it away from all negative embeddings in the current mini-bate batch and the memory queue Q:
B
exp(f, f /Te)
Let
M
log
(5)
B
E exp(f." ffy// ///+T)+ En exp(f. fg/Te),
where B is the mini-batch size, f. and f.' are the online and momentum embeddings of the i-th sample, fo' indexes other in-batch embeddings, f runs over embeddings stored in the memory queue Q, and Te 0 is the temperature controlling the sharpness of the similarity distribution.
Finally, the overall loss for TAB combines the mmcertainty-weigheee consistency loss and the con- trastive loss: Cest aLcons + BLetr. The algorithm of TAB is given in Algo
4 EXPERIMENTS
4.1 DATASETS AND METRICS
DTIGN. We first adopt the DTIGN dataset (Yin et al. 2024), which focuses on scaffold- OOD evaluation. Training and test sets are explicitly split according to the molecular scaffolds of small molecules, ensuring that test molecules have distinct scaffolds from those seen during training. Specifically, DTIGN comprises protein targets (II, 12, 13, 14, 15, E1, E2, and E3). Within each subset, hundreds to thousands of ligands are associated with the corresponding protein and target label IC50 or EC50-
SIU 0.6. We then employ the SIU 0.6 dataset (Huang et al., 2025), which is designed for protein- based OOD evaluation. fixed test set is provided, and to ensure OOD generalization, any proteins in the training set with sequence identity greater than 60% to the test set are removed. Specifically, for the K; task, there are 33,595 training samples and 3,153 test samples, corresponding to 195 training proteins and 10 test proteins. For the Kg task, there are 12,333 training samples and 1, 1,723 test samples, corresponding to 161 training proteins and 7 test proteins.
OODgOOD. Finally, we evaluate DrugOOD [Ji al. 2023) on the sructure-based affinity prediction dataset. For the assay subset, each domain corresponds a distinct experimental assay. The training set contains 55 such domains with 5,541 samples, while the test set includes 111 domains with 3,047 samples. For the protein subset, each domain represents a unique protein. The training set consists of 29 proteins with 5.524 samples, and the test set comprises 66 proteins with 3,006 samples. Notably, there is no overlap domain between the training and test sets.
The original dataset is simplified into binary classification task by discretizing continuous bioactiv- ity measurements. We restore it to its natural regression form, which better preserves the continuous signals and maintains sensitivity subtle yet biologically meaningful variations. In addition, since

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Table 2: Comparison of RMSE, Pearson's R, and Kendall's Tau results for different methods DTIGN across multiple tasks. The best performance is highlighted in bold.
Datasct
Metric
Method
Avg
11
12
13
14
15
E1
E2
E3
ERM
1.282
1.672
1.474
1.167
1.125
0.855
0.864
1.231
1.209
IRM
1.478
1.873
1.744
1.290
1.150
0.918
0.830
1.265
1.318
GroupDro
1.447
1.644
1.675
1.268
1.232
0.885
0.837
1.324
1.289
CIA-LRA
1.317
1.621
1.684
1.221
1.239
0.909
0.834
1.353
1.272
CaNet
1.573
1.682
1.609
1.180
1.144
0.935
0.855
1.373
1.294
RMSE L
DANN
1.387
1.746
1.738
1.238
1.169
0.949
0.805
1.295
1.291
AFSE
1.461
1.712
1.665
1.497
1.348
0.963
0,919
1.287
1.357
Mixup-GINN
1.435
1.773
1.820
1.461
1.177
0.970
0,973
1.354
1.370
EERM
1.326
1.540
1.576
1.440
1.232
0.863
0.854
1,281
1.264
GNN
1.277
1.595
1.681
1.227
1.167
0.874
0.832
1.352
1.251
TAB
1.180
1.481
1.433
1.217
1.121
0.841
0.778
1.207
1.157
ERM
0.592
0.533
0.474
0.389
0.349
0,200
0,462
0.316
0.414
IRM
0,306
0.465
0.343
0.320
0.305
-0.033
0.355
0.236
0.287
GroupDro
0,332
0.464
0.332
0.298
0,256
0.145
0.376
0.258
0.308
CIA-LRA
0.427
0.438
0.285
0.361
0.196
0.409
0.286
0.314
CaNet
0.427
0.443
0.314
0.293
0,298
0.200
0.440
0.191
0.326
R
DANN
0.478
0.476
0.507
0.257
0.311
0.088
0.425
0.257
AFSE
0.546
0.373
0.382
0.302
0.316
0.133
0.415
0.220
0.336
Mixup-GNN
0,308
0.395
0.296
0.193
0,255
0.035
0.109
0.110
0,213
EERM
0.469
0.559
0.352
0.277
0.302
0.194
0.386
0.262
0.350
SR-GNN
0.482
0.480
0.351
0.342
0.332
0.209
0.422
0.286
0.363
TAB
0.614
0.605
0.537
0.401
0.359
0.216
0.507
0.349
0.448
ERM
0.417
0.393
0.314
0.273
0.226
0.139
0.330
0.271
0.295
IRM
0.223
0.306
0.237
0.232
0.216
0.005
0.250
0.204
0.209
GroupDro
0.238
0.306
0.226
0.218
0.167
0.083
0.274
0.227
0.217
CIA-LRA
0.310
0.322
0.194
0.258
0.178
0.120
0.308
0.245
0.234
CaNer
0.301
0.321
0.175
0.206
0.203
0.115
0.320
0.138
0.223
DANN
0.353
0.317
0.349
0.178
0.218
0.090
0.308
0.232
0.256
AFSE
0.375
0.270
0.249
0.211
0.221
0.069
0.293
0.208
0.237
Mixup-GNI GNN
0.217
0.276
0.197
0.201
0.168
0.020
0.100
0.132
0.164
EERM
0.331
0.410
0.232
0.158
0.197
0.136
0.282
0.248
0.250
SR-GNN
0.370
0.348
0.235
0.238
0.227
0.160
0.308
0.256
0.268
TAB
0.427
0.419
0.356
0.279
0.245
0.138
0.350
0.282
0.312
DrugOOD only provides ligand SMILES and protein amino acid sequences, the interaction informa- tion between ligands and targets cannot directly modeled. To address this limitation, we perform molecular docking. Detailed procedures for docking are provided in Appendix C
Metrics. To ensure fair comparison with previous works, we adopt widely used evaluation metrics in bioactivity prediction, including root mean square error (RMSE), Pearson correlation coefficient R, Kendall's Tau correlation coefficient т. Following the original SIU 0.6 paper, we additionally report absolute error (MAB) and Spearman correlation coefficient (p) for it.
4.2 EXPERIMENTAL SETTINGS
We compare TAB with several baseline approaches. The standard empirical risk minimization (ERM) paradigm included as basic reference. In addition, we evaluate invariant learning meth- ods IRM (Arjovsky et al. 2019), GroupDro (Sagawa et al. 2019). CIA-LRA (Wang et al. 2024a) and CaNet (Wu et al. 2024b); domain generalization methods DANN (Ganin et al. 2016) and
AFSE (Yin et al. 2022); graph data augmentation methods Mixup-GNN (Wang et al. 202 and graph OOD methods EERM (Wu et al., 2022) and SR-GNN (Zhu et al. 2021). all of which require training with access to source data. In contrast. TAB operates in a source-absent setting. For fair comparison, we adopt DTIGN (Yin et al. 2024) as the backbone model across all experiments. Additional details and hyperparameter settings provided in Appendix

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Table 3:
Comparison of MAE, Pearson's 's R, Kendall's Tau т, and Spearman's ' p results across different methods SIU 0.6 for Ka and K; tasks. The best performance is highlighted in bold.
Table 4: Comparison of RMSE, Pearson's 's R. and Kendall's Tau T across different methods DrugOOD. The best performance is high- lighted in bold.
Metric
Metric
Dataset
Mothod
Dataset
Method
MAEI
RT
-1
PT
RMSE.
R1
ERM
1.095
0.320
0.215
0.319
ERM
1.506
0.119
0.063
IRM
IRM
1.541
0.229
0,142
GroupDro
GroupDro
1.666
0.010
CIA-LRA
1.175
0.174
0.125
0.184
CIA-LRA
1.487
0.265
0.170
Ka
CaNet
1.171
0,245
0.185
0.271
Assay
CaNet
1.437
0.269
0.153
DANN
1.205
0.025
0.038
0.060
DANN
1.612
-0.061
-0.032
AFSE
1.069
0.304
0.247
0.374
AFSE
1.519
-0.027
.0.005
Mixup-GNN
1.100
0.361
0.253
0.386
Mixup-GNN
1.539
-0.015
-0.004
EERM
1.284
0.098
0.033
0.052
EERM
1.525
0.236
0.111
SR-GNN
1.028
0.384
SR-GNN
0.145
TAB
1.055
0.393
0.283
0.419
TAB
1.552
0.388
0.230
ERM
1.653
0.123
0.060
0.091
ERM
1.367
0.018
0.032
IRM
IRM
1.483
0.092
0.061
GroupDro
GroupDro
1.459
0.021
0,015
CIA-IRA
1.679
0.100
0.036
0.055
CIA-LRA
1.492
-0.020
-0.030
K
CaNet
0.034
Protein
CaNet
DANN
1.727
0,060
0.074
0.110
DANN
1.327
0.065
0,042
AFSE
1,652
0.089
0.070
0.104
AFSE
1.351
-0.032
-0.005
Mixup-GNN
1.600
0.091
0.043
0.065
Mixup-GNN
1.578
0.009
-0.021
EERM
1.805
40.027
0.026
0.040
EERM
1.369
0.110
0.076
SR GNN
1.612
0.003
00011
0.015
SR GNN
1.536
0.051
0.036
1.415
0.141
TAB
1.319
4.3 EXPERIMENTAL RESULTS
We first compare TAB with existing methods under the scaffold OOD setting on the DTIGN dataset. As shown in Table 2 the average performance reveals that existing OOD approaches fail to deliver gains over standard ERM. In contrast, TAB achieves superior results across nearly all evaluation metrics. Specifically. TAB reduces RMSE by 4.3%, increases Pearson's R by 8.2%, and improves Kendall's Tau by 5.8% on average compared to the best baseline. These findings demonstrate that TAB not only lowers prediction error but also substantially enhances correlation- mea- sures, yielding more reliable rankings of pocket-ligand interactions. Importantly, the improvements are consistent across diverse protein targets, highlighting the robustness and generalizability of our method in the scaffold OOD setting.
Besides, evaluate TAB on SIU 0.6 dataset under the protein OOD setting. As reported in Table TAB consistently outperforms alternatives on both tasks. We note that IRM and GroupDro are not applicable due to the lack of domain partitioning in the SIU 0.6 dataset, and thus their results are denoted as '-' For Ka task, TAB achieves the highest Pearson's R (0.393), Kendall's Tau r (0.283), and Spearman's 's p (0.419), outperforming the strongest baseline by a clear margin while maintaining competitive MAE. Similarly, for the K; task, TAB delivers substantial improvements, raising Pearson's R from 0.123 0.141, Kendall's Tau from 0.060 0.115, and Spearman's from 0,091 to 0.175. These consistent gains across multiple correlation- based metrics confirm that TAB more accurately captures the relative binding affinities, thereby yielding more reliable predictions in OOD scenarios.
Finally, we present the results on DrugOOD in Table where TAB achieves the best overall perfor- mance across nearly all metrics. On the assay task, while TAB shows a slightly higher RMSE (1.552) compared to ERM (1 (1.50)), it substantially improves the correlation metrics, increasing Pearson's R from 09 to 0.388 and Kendall's Tau from 0.063 to 0.230. On the protein task, TAB delivers consistent gains, lowering RMSE from 1.367 to 1.319 (a 3.5% reduction) and boosting Pearson's 's R from 0.018 to 0. and Tau 7 from 0.032 to 0.073. Together with the scaffold and protein OOD gains observed on DTIGN and SIU 0.6, these results highlight TAB's s robustness and effectiveness across diverse types of domain shift.
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4.4 IN-DEPTH ANALYSIS OF TAB
4.4.1 ABLATION STUDY
In our proposed TAB, we incorporate both uncertainty-weigheed consistency learning and con- trastive learning obtain more robust predictions. To assess the contribution of each component, we conduct ablation experiments on the DTIGN dataset, with results summarized in Table
Table 5: Ablation study on DTIGN dataset. "w/o contr" and "w/o cons" denote removing the con- trastive learning and consistency learning modules, respectively. The best performance is high- lighted bold.
TAB
wh contr
TAB cons
ERM
Dataset
RMSEL
RT
rf
RMSEL
RT
RMSEL
R1
rf
RMSE
RT
7?
1.180
0.614
0.427
1.221
0.594
0.420
1.265
0.610
0.429
1.282
0.592
0.417
1,481
0.605
0.419
1.493
0.605
0.369
1.672
0.533
0.393
13
1.433
0,537
0.356
1.526
0.488
0.317
1.441
0.482
0.303
1.474
0.474
0.314
0.395
15
1,121
0.359
0.245
1.174
0.325
0,207
1.154
0.358
0,240
1.125
0.349
0.226
0.841
0.216
0.138
0.894
0.196
0.126
0.202
0.855
0.200
0.139
E2
0.778
0.507
0.350
0.785
0.421
0.275
0,926
0.482
0,340
0.864
0.462
0.330
E3
1.207
0.349
0.282
1,260
0.319
0.274
1.273
0.286
0.263
1.231
0.316
0.271
Avg
1.157
0.448
0.312
1.191
0.432
0.285
1.201
0.427
0.295
1.209
0.414
0.295
We can see that each component positively contributes to the overall performance, with the full model TAB consistently achieving the best results compared with its two variants, TAB w/o contr and TAB w/o cons. Interestingly, we also observe that incorporating either consistency or contrastive learning in isolation may occasionally underperform the baseline. This phenomenon is not surpris- ing: applying only consistency learning can lead to overly compressed representations, thereby weakening discriminability. while applying only contrastive learning may amplify spurious differ- ences without proper regularization. When combined, the two modules act in complementary manner, promoting the learning of invariant yet informative representations that more faithfully cap- ture the underlying bioactivity signals.
4.4.2 CASE STUDY ON BINDING REGION ATTRIBUTION
We employ a perturbation- based attribution strat- egy to assess whether TAB effectively focuses the true binding region. For each pocket-ligand complex, we randomly remove subset of ligand atoms, their connected pocket atoms, and the as- sociated inter-molecular edges, thereby simulat- ing scenarios where key binding site information missing. The model predictions on the original and perturbed inputs are denoted as Fon and Sper- and their difference is defined as AÚ = Don Mper- Larger Aÿ indicates greater sensitivity to critical binding sites, which is desirable. We report the average value Aÿ across all sampled subsets the DTIGN dataset, shown in Figure Since atom and edge removal disrupts essential bind- ing interactions, the bioactivity is expected de- crease, and thus Aÿ should generally be positive. Interestingly, negative AÚ are observed in the ERM baseline, highlighting its flawed reliance on irrelevant sites. By comparison, TAB consistently yields significantly higher Ay, demonstrating its stronger and more reliable focus the relevant interaction regions.
Figure Prediction variance of TAB and ERM baseline under perturbation--asee attribution.
ERM
0.739
TAB
0.5
0.314 0.341
0.260
0.174
0.0
-1.0
-1.5
11
12
13
Datasets

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Published as a conference paper at ICLR 2026
5 CONCLUSION
In this paper, we address the research gap concerning source data-absent out-of-domain (OOD) bioactivity prediction. We propose TAB, a test-time adaptation framework that captures molecular interaction-awant features by focusing on bioactivity-relevan regions. By combining uncertainty- weighted consistency learning with contrastive optimization, TAB suppresses spurious correlations while enhancing feature discriminability. enabling robust and bioactivity-aware representations. Ex- tensive experiments demonstrate effectiveness across diverse domain shifts, providing a practical source-absent solution for real-world bioactivity prediction.
ACKNOWLEDGMENTS
We gratefully acknowledge Nanyang Biologics Pte. Ltd. (NYB) for providing travel support to attend the conference.
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A ADDITIONAL EXPERIMENTA ANALYSIS
A.1 SENSITIVITY ANALYSIS OF LOSS WEIGHTS
We conduct a sensitivity analysis on the consistency weight a and the contrastive weight to exam- ine the robustness of TAB. To ensure fair comparability across different settings, fix 3
and evaluate a wide range of a/3 configurations. The detailed results are reported in Table
Table Sensitivity analysis of the consistency weight a and contrastive weight 8.
Dataset
Method
a
B
RMSEL
R1
r
ERM
1.282
0.592
0.417
TAB
0.2
1.8
1.265
0.590
0.424
TAB
0.6
1.4
1.374
0.602
0.424
II
TAB
1.0
1.0
1.180
0.614
0.427
TAB
1.4
0.6
1.165
0.614
0.417
TAB
1.8
0.2
1.289
0.605
0.426
ERM
1.672
0.533
0.393
TAB
0.2
1.8
1.460
0.603
0.420
TAB
0.6
1.4
1.453
0.601
0.427
12
TAB
1.0
1.0
1.481
0.605
0.419
TAB
1.4
0.6
1,408
0.597
0.439
TAB
1.8
0.2
1.427
0.599
0.411
ERM
1.474
0.474
0.314
TAB
0.2
1.8
1.448
0,463
0.327
TAB
0.6
1.4
1.458
0.458
0.319
13
TAB
1.0
1.0
1.433
0.537
0.356
TAB
1.4
0.6
1.552
0.498
0.340
TAB
1.8
0.2
1.588
0.510
0.347
Overall, TAB exhibits stable performance across a broad range of a/3 ratios, suggesting that the proposed framework is not overly sensitive to the exact weighting between consistency regulariza- tion and contrastive learning. In particular, the default setting a 1 and f 1 consistently achieves competitive or near-optimal results across datasets, validating our choice of equal weighting in the main experiments.
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A.2 EFFECT OF UNCERTAINTY WEIGHTING
We further conduct an ablation study to assess the impact of uncertainty-aware weighting. Specif- ically, replace the uncerfainty-wiighnee as TAB w/o uw. As shown in Table 7 removing uncertainty weighting leads to consistent perfor- mance degradation across all datasets. This observation confirms that uncertainty-awi weighting plays a critical role in TAB, as it adaptively down-weights unreliable predictions during test-time optimization, thereby reducing the risk of propagating noisy or misleading updates.
consistency loss with uniform weighting, referred to
Table 7: Ablation study on uncertainty-weighhingg
Dataset
Method
RMSE
Rf
ERM
1.282
0.592
0.417
I1
TAB w/o uw
1.232
0.591
0.422
TAB
1.180
0.614
0.427
ERM
1.672
0.533
0.393
12
TAB w/o
1.609
0.576
0.416
TAB
1.481
0.605
0.419
ERM
1.474
0.474
0.314
13
TAB w/O uw
1.435
0.484
0.336
TAB
1.433
0.537
0.356
A.3 EFFECT OF EMA AND MASKING STRATEGIES
We conduct ablation studies analyze the impact of the EMA update strategy and the proposed masking mechanism. Specifically, TAB w/o EMA removes the EMA-based parameter update, TAB (substitution) replaces atom masking with random atom substitution, TAB (coord. jitter) applies random perturbations to atomic 3D coordinates instead of masking, and TAB (mask) corresponds to original design. The results summarized in Table
Table 8: Effect of EMA update and masking strategies.
Dataset
Method
RMSE
Rt
?
ERM
1.282
0.592
0.417
TAB w/o EMA
1.272
0.572
0.418
TAB (substitution)
1.232
0.592
0.412
TAB (coord. jitter)
1.470
0.503
0.347
TAB (mask)
1.180
0.614
0.427
ERM
1.672
0.533
0.393
TAB w/o EMA
1.585
0.556
0.404
12
TAB (substitution)
1,513
0.581
0.416
TAB jitter)
1.665
0.543
0.398
TAB (mask)
1.481
0.605
0.419
ERM
1.474
0.474
0.314
TAB w/o EMA
1.467
0.474
0.323
I3
TAB (substitution)
1,449
0.477
0.319
TAB (coord. jitter)
1.493
0.446
0.299
TAB (mask)
1.433
0.537
0.356
A.4 EFFECT OF BATCH SIZE
We investigate the impact of batch size by evaluating four different setting (4, 8, 16, and 32). Across all datasets, TAB consistently outperforms the pretrained ERM baseline, demonstrating strong
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bustness batch size variations shown in Table Notably, observe rise-then-fall trend as the batch size increases: moderate batch sizes generally yield the best performance, while overly large batches lead slightly diminished gains.
Table 9: Effect of batch size.
Dataset
Method
Batch Size
RMSEL
R1
ERM
1.282
0.592
0.417
TAB
4
1.186
0.604
0.442
I1
TAB
8
1.166
0.627
0.448
TAB
16
1.180
0.614
0.427
TAB
32
1.272
0.601
0.431
ERM
1.672
0.533
0.393
TAB
4
1.641
0,550
0.392
12
TAB
8
1.453
0.581
0.409
TAB
16
1.481
0.605
0.419
TAB
32
1.508
0.566
0.396
ERM
1.474
0,474
0.314
TAB
4
1.443
0.487
0.328
13
TAB
8
1.438
0.483
0.334
TAB
16
1.433
0.537
0.356
TAB
32
1.446
0.490
0.400
B DETAILS OF BASELINE METHODS
IRM (Arjovsky et al. 2019
Invariant Risk Minimization (IRM) is a learning paradigm developed to tackle the poor out-of- distribution (OOD) generalization of traditional Empirical Risk Minimization (ERM); ERM relies on the independent and identically distributed (IID) assumption, which often leads it to absorb spu- rious correlations from training data (e.g., cows being disproportionatel associated with green pastures and camels with deserts in image classification) and thus fail when test data distribution shifts, whereas IRM leverages multiple distinet training environments (e.g., different data collec- tion locations, times, experimental conditions) to learn invariant causal correlations instead of environment-specific spurious ones. It aims to find data representation such that the optimal clas- sifier built on this representation is consistent across all training environments, and is formulated as a constrained optimization problem that balances two goals: minimizing predictive eтror on train- ing data (aligning with ERM) and imposing penalty to enforce the invariance of the classifier across environments, thereby enhancing models' ability to generalize to unseen test distributions by prioritizing invariant causal relationships over spurious ones.
GroupDro (Sagawa et al. 2019
Group Distributionally Robust Optimization (GroupDRO) is a group-level variant of Distribution- ally Robust Optimization (DRO) designed to address the limitation that overparameterizee neural networks often achieve high average accuracy but fail on atypical data groups (e.g., due spurious correlations). It minimizes the worst-case training loss across pre-defined data groups, enabling ro- bustness group shifts. Critically, naive GroupDRO underperforms overparameterized regimes; thus, it requires coupling with strong regularization penalties to reduce group-specific generalization gaps and boost corst-group test accuracy while preserving high average accuracy. Additionally, an efficient stochastic optimization algorithm with convergence guarantees is proposed to scale Group- DRO to large models and datasets.
CIA Wang al. 2024a)
To tackle the limitation that traditional invariant learning methods (e.g.. IRM) fail in node- level graph OOD generalization due to insufficient class-conditional invariance constraints, Cross- environment Intra-class Alignment (CIA) proposed. CIA eliminates spurious features by aligning representations of same-class nodes from different environments these nodes share similar invari-
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ant causal patterns but differ in spurious features. By enforcing this class-conditional invariance (lacking in IRM), CIA accurately identifies invariant causal patterns. For node-level OOD scenarios where environment labels unavailable, CIA-LRA (Localized Reweighting Alignment) is further developed. It avoids environment labels via three key designs: 1) aligning only local node pairs (2-6 hops) to prevent invariant feature collapse; 2) reweighting pairs based neighborhood label dis- tributions; 3) integrating an invariant subgraph extractor for refined aggregation. It is theoretically guaranteed by a PAC-Bayesian OOD error bound.
CaNet [Wu et al. 2024b)
Causal Intervention for Network Data (CaNet) to tackle the OOD generalization issue of GNNs, where GNNs suffer performance degradation due to latent environmental confounding bias (lead- ing to over-reliance on environment-sensitie spurious correlations between ego-graph features and node labels). Without prior environment labels, CaNet integrates an environment estimator (infer- ring pseudo-environments from ego-graphs, and a mixture-of-expert GNN predictor (dynamically selecting propagation branches via pscudl-envvrromments) Its causal inference-derived objective mitigates confounding bias and learns invironment-invarinnn relations.
DANN GGanin et al. 2016)
Domain Adverssadd Neural Networks (DANN) is a domain adaptation approach for neural net- works, aiming to learn features that are discriminative for the source-domain task yet invariant to source-target domain shifts. It enhances feed-forward architectures with a gradient reversal layer (GRL) and jointly optimizes three components: feature extractor, label predictor (for source-task classification), and domain classifier (to distinguish source/target domains). The feature extrac- tor is trained to minimize the source label loss while maximizing the domain classifier loss (via GRL reversing gradients during backpropagation), enabling adaptation with labeled source data and unlabeled target data (no target labels needed).
AFSE Yin et al. 2022)
Adversarial Feature Subspace Enhancement (AFSE) is proposed to tackle the poor generalization of deep graph learning (DGL) models in predicting GPPRR-targetin ligand bioactivities, a problem caused by sizel-sized/biaseed training datasets and inherent activity cliffs. dynamically generates rich representations in new feature subspaces through bidirectional adversarial learning, and min- imizes the maximum loss of molecular divergence and bioactivity to ensure local smoothness of model outputs.
Mixup-GNN (Wang et al. 2021
This work introduces Mixup strategies tailored for both node and graph classification tasks. For node classification, a two-branch Mixup graph convolution employed to combine paired nodes' receptive-field subgraphs. To mitigate interference, two-stage framework is adopted: the first stage obtains neighbor representations through a standard GNN, while the second stage applies Mixup convolutions using these representations. For graph classification, the method interpolates graph-level representations directly in the semantic space, which helps reduce overfitting in GNNs.
EERM (Wu al. 2022)
Risk Minimization (EERM) targets OOD generalization for node-level graph tasks. It employs multiple adversarial context generators (graph structure editors) maxi- mize risk variance across virtual environments while minimizing mean risk, enabling GNNs to ex- trapolate from a single observed environment. The method has theoretical guarantees for valid OOD solutions and shows effectiveness in handling shifts from artificial spurious features, cross-domain transfers, and dynamic graph evolution across diverse GNN backbones.
Sxplore-to-Extrappt
SR-GNN (Zhu et al. 2021 Shift-Robust GNN (SR-GNN) designed to tackle distributional shift from biased training data in GNN-based semi-supervised learning-a critical issue overlooked by most existing GNNs. As a general framework, it works for both deep traditional GNNs (e.g., GCN) and shallow linearized GNNs: for deep models, it uses Central Moment Discrepancy (CMD) regularization reduce repre- sentation shift between biased training and unbiased IID samples; for linearized models, it employs instance reweighting to improve training sample representativeness.
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C DETAILS OF DATASETS
DTIGN dataset Yin et al. 2024)
The dataset of DTIGN is specifically designed support significant task: improving bioactiv- ity prediction by integrating drug-target interaction information, which addresses the limitation of traditional datasets that only focus on ligand structures. forms a unique benchmark to en- able interaction-aware bioactívity prediction evaluation. The bioactivity data is derived from the ChEMBL database, screened to target single proteins with over 1000 bioactive small molecules (re- sulting in 900 target proteins from 6778 human proteins in ChEMBL), and preprocessed to obtain 6.75 million valid records covering metries like IC50 and FCC00- The DTIGN dataset contains eight protein targets (denoted as 11, 12, 13, 14, 15, E1, E2, and F3), each treated as independent sub- set. Within each subset, the ligand scaffolds are diverse, and the test set was manually constructed to evaluate the ability to identify ligands with promising bioactivity. Consequently, the dataset is inherently non-L.L.D The information of DTIGN is shown in Table 10
Table 10: Summary statistics of the DTIGN dataset
Subset
Task
#Data points
IC5o
926
I2
ICso
1,509
I3
IC50
3,501
14
ICso
5,920
15
1C30
9,336
E1
EC50
996
E2
EC30
1,639
E3
EC50
3,532
SIU dataset (Huang et al. 2025)
The SIU dataset is recently introduced benchmark for bioactivity prediction that provides multiple types of bioactivity measurements. Two versions are available: SIU-0.9 and SIU-0.6, where the number denotes the sequence similarity threshold applied to remove test samples that are overly similar to those in the training set. In this study, we use SIU-0.6, since the sequence would be much more diverse, and we choose Ka and K₃ as the target label. The information of DTIGN is shown in Table
Table 11: Summary statistics of the SIU subsets
Dataset
Task
Subset
#Data points
train
33,595
K
valid 3,787
test
3,153
SIU-0.6
train 12,333
Ka valid 1,422
test
1,723
DrugOOD dataset (Ji et al. 2023
DrugOOD is systematic diitti-distiibuttinn (OOD) dataset curator and benchmark tailored for Al-aided drug discovery (AIDD), with specific focus the critical task of ttrgettrget binding affinity prediction involving both mmoll-molecule compounds and macromolecular protein targets. For the present study, we utilized subsets from structure-based affinity prediction (SBAP) task of DrugOOD, specifically focusing on datasets under the ECso measurement and core noise level. Detailed statistics of the utilized subsets are shown in the Table 12
should be noted that the original DrugOOD dataset for the SBAP task lacks structural information for proteins and ligands, providing only protein sequences and ligand SMILES. For each protein,
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retrieve its PDB ID using the UniProt identifier and obtain the corresponding PDB structure. For each ligand, we generate ligand 3D structures by converting SMILES using the RDKit toolkit. Then, potential binding sites identified by FPocket (Le Guilloux al. 2009) define the docking box. Finally, we generate docking pocket-ligand complexes with VPn 2.1 (Tang et al. 2024).
Table 12: Summary statistics of the DrugOOD subsets
Dataset
#Training
#Validation
#Testing
#Training
#Validation Env
#Test
#Label
RC50-assay-core
6,387
3,562 3,544
3,527
51
122
FC5o
CCC50-protcin-corg
6.420
3,505
29
28
72
EC50
D DETAILS OF METRICS
RMSE
Root Mean Squared Error (RMSE) is a widely used metric for evaluating regression models. It measures the square root of the average squared difference between predicted values and actual values. RMSE penalizes larger errors more strongly, making it sensitive to outliers. A lower RMSE indicates better of the model to the data.
RMSE
y1
41)
72
where n is the number samples, yi is the true value, and ÿi is the predicted value.
Pearson Correlation Coefficient
The Pearson Correlation Coefficient (R) is statistical measure that evaluates the linear relationship between two variables. Its value ranges from -1 to +1: value close +1 indicates strong positive correlation, a value close to -1 indicates strong negative correlation, and a value near suggests little or no linear correlation.
i= (I: F) (y) 5)
R
Σ (zi - -))² EE (ys 17)
where v; and are the observed values of the two variables, & and are the means of x and y, n is the number of samples.
Kendall's Tau Correlation Coefficient
Kendall's Tau (7) is non-parametrie measure of the ordinal association between variables. It evaluates the similarity of the orderings of data when ranked by each variable. The coefficient ranges between - and +1, where value close +1 indicates strong agreement, -1 indicates strong disagreement, and 0 suggests no association.
C-D D-D
T
nnnn
where C' is the number of concordant pairs, D is the number of discordant pairs, and n is the total number data points.
MAE
Mean Absolute Error (MAE) is a common metric for evaluating regression models. It measures the average of the absolute differences between predicted values and actual values. Unlike RMSE, MAE treats all errors with equal weight, making it less sensitive to outliers. A lower MAE indicates better model performance.
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MAE Σ 1=1
where n is the number of samples, y/ is the true value, and Ús is the predicted value.
Spearman's 's Rank Correlation Coefficient
Spearman's Rank Correlation Coefficient (p) is a non-parametric measure of the monotonic rela- tionship between two variables. Unlike Pearson correlation, which captures linear dependence, Spearman correlation is based on the ranked values of the data. Its value ranges from -1 (perfect negative correlation) to +1 (perfect positive correlation), with indicating correlation.
1= d
=
(n²
where d; = R(x;)) R(ys) is the difference between the ranks of æ; and y/s and n is the total number of samples.
IMPLEMENTATION DETAILS
For the uncertainty uncertainty-weighted regularization, we pre-store database dropout- tropout-bbssed outputs from the original source model with the original inputs to improve computational efficiency. Specifically, we apply a dropout rate of and perform five forward passes to compute the standard deviation. For contrastive learning, the momentum of the key encoder is 0.999, and the temperature for the contrastive loss is 0.I. We set both loss weights for consistency learning and contrastive lcarning to 3 = 1.0 across all experiments. Model training is conducted using the AdamW optimizer with a StepLR scheduler. All experiments are implemented Py with Python 3.8 and executed on an Ubuntu server equipped with four NVIDIA GeForee RTX 4090 GPUs.
F LIMITATIONS
A key limitation of TAB is its reliance pocket-ligand complex information. When experimentally resolved structures are unavailable, docking simulations must be performed to generate conforma- tions, adding computational overhead. Nevertheless, with GPU acceleration, docking time remains acceptable. Developing methods that can directly leverage sequence information without complex structure modeling remains an important direction for future work.
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Reviews:
[{"review_id": "cVVm9SONIr", "reviewer": "Reviewer_1xoV", "summary": "This paper introduces TAB, a test-time adaptation framework to improve out-of-domain (OOD) bioactivity prediction in scenarios where the original source data is inaccessible. The proposed method uses an uncertainty-weighted consistency strategy and contrastive learning to adapt a pre-trained model, encouraging it to focus on important binding regions while ignoring spurious substructures. Experiments across multiple benchmarks demonstrate that this source-data-absent approach significantly outperforms existing methods in scaffold, protein, and assay-based OOD settings.", "questions": "See Weaknesses", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. This work develops the first Test-Time Adaptation (TTA) method tailored to bioactivity prediction, presenting a novel and practically relevant problem formulation.\n\n2. The proposed framework performs self-supervised optimization during testing without requiring complex architectural changes or extensive additional labeled data, making the overall approach simple and easy to follow.\n\n3. The evaluation on three OOD benchmarks with different focuses covers various distribution shift scenarios, demonstrating strong generalizability and robustness.", "weaknesses": "1. The loss weights for consistency learning ($\\alpha$) and contrastive learning ($\\beta$) are fixed at 1.0 across all experiments. It would be helpful to include sensitivity analysis for these key hyperparameters to validate model robustness.\n\n2. The existing ablation study (Table 5) validates the necessity of consistency learning and contrastive learning modules. Could authors further isolate uncertainty weighting for separate validation, as this appears to be an important contribution?\n\n3. How is the pre-trained encoder in Fig. 2 or Section 3.2 obtained?\n\n4. Minor:\n(a) Typos in Equation 3;\n(b) Suggest adding an analysis of TAB's efficiency and computational resource consumption.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "5ccsj532bl", "reviewer": "Reviewer_Weoy", "summary": "This paper tackles the important problem of out-of-domain (OOD) generalization in bioactivity prediction under a source data-absent setting. The authors propose TAB, a test-time adaptation framework combining (1) uncertainty-weighted consistency learning and (2) contrastive optimization. Extensive experiments on DTIGN, SIU 0.6, and DrugOOD show consistent gains over a variety of state-of-the-art OOD generalization baselines.", "questions": "please respond to the weakness", "limitations": "", "rating": 6, "confidence": 5, "soundness": 2, "presentation": 4, "contribution": 3, "strengths": "1.The writing is clear and figures are well-designed, facilitating understanding.\n\n2.The chosen application scenario is highly relevant and meaningful.\n\n3.The experimental results are strong and demonstrate the effectiveness of the proposed method", "weaknesses": "1.Realistic source data-absent scenario. The proposed source-absent setting assumes that the training data are inaccessible. While this may occur in industrial contexts, for example when dealing with large-scale proprietary pre-trained models, the experiments in the paper mainly use a small DTIGN model trained on publicly available ChEMBL data. It is unclear whether TAB’s effectiveness in true source-absent scenarios can be reliably inferred. Evaluating TAB on larger pre-trained models would strengthen the paper.\n\n2.Ablation studies are insufficient. The paper does not provide ablations for the update strategy or the newly proposed masking strategy. Including these analyses would clarify the contributions of each component.\n\n3.Batch update and sequential sensitivity. TAB accumulates model updates using EMA and memory to reduce sensitivity. An ablation study examining the effects of batch size and test sample order would help assess robustness and stability.\n\n4.Clarification on base model usage across datasets. It is unclear whether the same pre-trained DTIGN model is applied to all three datasets or if models are re-trained for each dataset. If the former, the authors should clarify potential overlaps or differences between training and test distributions, particularly for the latter two datasets.\n\n5.Comparison baselines. The reported comparisons are all against source-available methods. It would be useful to include comparisons with source-absent baselines or other approaches designed for realistic deployment constraints.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "JESG0p7vY4", "reviewer": "Reviewer_oGSF", "summary": "In the paper, the authors proposed methods for test-time adaptation for OOD ligand-target prediction under the assumption that  the accesses to source (training) data are prohibited. The proposed method called TAB employs data augmentation by randomly masking atoms in the binding graphs and forcing the consistency of the representation of  augmented data and the original data. Besides, contrastive learning was also proposed to enhance the representation discriminabilty and prevent feature collapse.\n\nIn the experiments, the authors studies the effectiveness of TAB against baseline approaches across DTIGN, SIU 0.6, and DrugOOD benchmark datasets. The results show that their proposed method outperforms baseline approaches with a significant margin.", "questions": "Could you please analyse the effects of having very limitted test data say few-shot settings on the proposed methods?", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 1, "strengths": "The application domain is important, the problems the authors tackle is a challenging problem.", "weaknesses": "The assumption that source data is not available but the pretrained model is available limits the application of the work in practice. Accesses to the model especially the weights of the models usually also come with accesses to the training data as well. Making a strong assumption that is not realistic results in a small variation of  the problem  but limit its scope of application.\n\nThe methods proposed in the work such as data augmentation via randomly masking atoms, consistency and contrastive training objectives are all standard techniques.  Therefore the technical contribution of the works are limited.\n\nEven the paper assume that there is no accesses to source data  but it also assume that there is an access to numerous test data used for augmentation and training consistency/contrastive learning.  To perform such adaptation  it requires numerous test data and the OOD setting becomes less practically interesting because in practice OOD test instances are rare.\n\nUnder OOD, the assumption that pockets are known is a strong assumption as for new target even pockets are not known in advance. The given assumption restrict the application of the work to known targets.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "2Vnin4BcNw", "reviewer": "Reviewer_UabT", "summary": "The paper presents a method for test time adaptation for OOD protein-ligand binding prediction. The method consists in finetuning the encoder of a GNN architecture with two objectives: consistency (a transformation is applied to the input and the method learns to project augmented versions of the same input into the same embedding space, the loss is weighted by the internal certainty of the model, to minimize noise due to transformations that may deform the input) and contrastive learning (the method learns to project close together augmented views of the same input and further apart from augmented views of other inputs).", "questions": "Is there any specific reason that I may be missing as to why the title focuses on bioactivity rather than binding affinity prediction?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 1, "presentation": 3, "contribution": 2, "strengths": "1. The method is interesting. Its application domain, when no source data exists apart from the features at inference time is highly relevant and captures realistic scenarios.\n2. The evaluation datasets are relevant and baselines are comprehensive.", "weaknesses": "1. Title is misleading. I think that protein-ligand binding affinity is a more accurate description of the method rather than bioactivity. Some bioactivities like antibacterial, anticancer, allergenic, do not necessarily involve explicit protein-ligand interactions and this method is limited to modelling those types of interactions.\n2. Results should contain a measure of the dispersion of the results across multiple runs, otherwise it is impossible to determine the significance of the differences.\n3. Along the lines of 2, the contribution of the contrastive learning, or the consistency learning are difficult to determine. For example, from the baseline model (ERM) to TAB w/o cons there is a difference of 0.008 in RMSE, which does not seem significant, suggesting that the contrastive learning is not providing that much information. Similarly TAB w/o contr has a performance of 1.191 which is only 0.034 from the complete method. Looking at the results in Table 2, it seems like the standard deviation across datasets is higher than those improvements, indicating that there is not enough evidence to make any claims regarding the benefits of TAB.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)